System and method for adjusting insulin delivery

ABSTRACT

The embodiments described herein may relate to methods and systems for adjusting insulin delivery. Some methods and systems may be configured to adjust insulin delivery to personalize automated insulin delivery for a person with diabetes. Some methods and systems may be configured to adjust insulin delivery to a person with diabetes according to one or more conditions of an insulin delivery device. Some methods and systems may be configured to enable a lock-out mode where adjustment to insulin delivery to personalize automated insulin delivery is restricted.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims the benefit under 35 U.S.C. § 119(e) of U.S.Provisional Patent Application Ser. No. 62/446,241, filed Jan. 13, 2017,the disclosure of which is hereby incorporated herein in its entirety bythis reference.

TECHNICAL FIELD

This document relates to systems and methods for adjusting insulindelivery.

BACKGROUND

Diabetes mellitus is a chronic metabolic disorder caused by an inabilityof a person's pancreas to produce sufficient amounts of the hormone,insulin, such that the person's metabolism is unable to provide for theproper absorption of sugar and starch. This failure leads tohyperglycemia, i.e., the presence of an excessive amount of glucosewithin the blood plasma. Persistent hyperglycemia has been associatedwith a variety of serious symptoms and life threatening long-termcomplications such as dehydration, ketoacidosis, diabetic coma,cardiovascular diseases, chronic renal failure, retinal damage and nervedamages with the risk of amputation of extremities. Because healing isnot yet possible, a permanent therapy is necessary that providesconstant glycemic control in order to constantly maintain the level ofblood glucose within normal limits. Such glycemic control is achieved byregularly supplying external drugs to the body of the patient to therebyreduce the elevated levels of blood glucose.

Historically, diabetes is treated with multiple, daily injections ofrapid and long acting insulin via a hypodermic syringe. One or twoinjections per day of a long acting insulin is administered to provide abasal level of insulin and additional injections of a rapidly actinginsulin is administered before or with each meal in an amountproportional to the size of the meal. Insulin therapy can also beadministered using an insulin pump that provides periodic or continuousrelease of the rapidly acting insulin to provide for a basal level ofinsulin and larger doses of that same insulin at the time of meals.Insulin pumps allow for the delivery of insulin in a manner that bearsgreater similarity to the naturally occurring physiological processesand can be controlled to follow standard or individually modifiedprotocols to give the patient better glycemic control. In somecircumstances, an insulin pump device can store (via input from aclinician or a user) a number of settings (e.g., dosage parameters orother settings) that are customized by the physician for the particularuser.

People with diabetes, their caregivers, and their health care providers(HCPs) bear a great deal of cognitive burden in managing intensivemedicine therapy. Delivering the correct amount of the medicine at thecorrect time is an extremely challenging endeavor. Such deliveryrequires the patient to make dosing determinations multiple times perday and also requires a combination of the patient and the HCP torecalibrate the therapeutic parameters of the therapy on an episodictime frame that varies from individual to individual, and withinindividuals based on age and/or behavior (e.g., change in exercise,change in diet).

In light of the many deficiencies and problems associated with currentsystems and methods for maintaining proper glycemic control, enormousresources have been put into finding better solutions. A number of newtechnologies promise to mitigate some of the cognitive burden thatintensive insulin therapy now requires. Developing workable solutions tothe problem that are simple, safe, reliable and able to gain regulatoryapproval has, however, proved to be elusive. For years, researchers havecontemplated coupling a continuous glucose monitoring system with aninsulin delivery device to provide an “artificial pancreas” to assistpeople living with diabetes. Their efforts have yet to result in acommercial product. What has been needed is a system and method thatprovides a level of automatic control of drug delivery devices forimproved medicine delivery and glycemic control that is simple, safe,and reliable in a real world setting.

BRIEF SUMMARY

One or more embodiments of the present disclosure may include a system.The system may include an insulin delivery device and an insulindelivery control unit. The insulin delivery device may be configured todeliver insulin to a user of the insulin delivery device. The insulindelivery control unit may be associated with the insulin deliverydevice. In some embodiments, the insulin delivery control unit may beconfigured to determine a shelf-life risk score for undelivered insulinwithin the insulin delivery device; and based on the shelf-life riskscore exceeding a threshold, enabling a lock-out mode for locking outautomated modification of a baseline basal insulin rate for the user ofthe insulin delivery device until the insulin delivery device has freshinsulin.

Other embodiments of the present disclosure may include a method. Themethod may include determining a shelf-life risk score for undeliveredinsulin within an insulin delivery device; and based on the shelf-liferisk score exceeding a threshold, locking out automated modification ofa baseline basal insulin rate for a user of the insulin delivery deviceuntil the insulin delivery device has fresh insulin.

BRIEF DESCRIPTION OF THE DRAWINGS

Example embodiments will be described and explained with additionalspecificity and detail through the use of the accompanying drawings inwhich:

FIG. 1 illustrates an example diabetes management system;

FIGS. 2A and 2B illustrate additional details of the example system ofFIG. 1;

FIG. 3A illustrates an example set of curves illustrating potentialinsulin sensitivity factors (ISFs) based on a basal rate (BR);

FIG. 3B illustrates an example set of curves illustrating potentialcarbohydrate-to-insulin ratio (CR) based on BR;

FIGS. 4A and 4B illustrate an example visualization of a probabilitydistribution of a general BR, general CR, and general ISF;

FIG. 5 illustrates example visualizations of probability distributionsof CR and ISF for a given BR;

FIG. 6 illustrates an example view of various user interfaces that mayfacilitate entering one or more therapeutic parameters;

FIG. 7 illustrates an example user interface providing a recommendationof one or more therapeutic parameters;

FIG. 8 illustrates an example visualization of adjusting one or moretherapeutic parameters;

FIGS. 9A and 9B illustrate example user interfaces for entering a bolusdose;

FIG. 10 illustrates an example graph illustrating a visualization ofback-filling based on a correction bolus;

FIG. 11 is a flowchart of an example technique for adjusting basalinsulin delivery rates;

FIG. 12 is a flowchart of an example method of adjusting a basal insulinrate;

FIG. 13 is a flowchart of another example method of adjusting a basalinsulin rate;

FIG. 14 is a flowchart of an example method of providing arecommendation of one or more therapeutic parameters;

FIG. 15 is a flowchart of an example method of delivering insulin;

FIG. 16 is a flowchart of another example method of delivering insulin;

FIG. 17 is a flowchart of an example method of adjusting a basal insulinrate;

FIG. 18 is a flowchart of an example method of personalizing an insulindelivery rate based on a correction bolus;

FIG. 19 is an example model for calculating future blood glucose values;

FIG. 20 is an example user interface associated with methods and systemsfor delivery of insulin described herein;

FIG. 21 is a flowchart of an example method of using insulin deliveryprofiles;

FIG. 22 is a flowchart of an example of adjusting insulin deliveryrates;

FIG. 23 is a flowchart of an example of utilizing a fear of hypoglycemiaindex associated with method and systems for delivery of insulindescribed herein; and

FIG. 24 is a flowchart of an example of utilizing a temporary overrideassociated with methods and systems for delivery of insulin describedherein.

DETAILED DESCRIPTION

Methods and systems provided herein may simplify the selection andpersonalizing of therapeutic parameters, such as basal rate (BR),carbohydrate-to-insulin ratio (CR), and insulin sensitivity factor(ISF), used when making insulin dosage decisions for a person withdiabetes (PWD). Various examples of personalizing therapeutic parametersare described herein. Methods and systems provided herein can use apersonalized BR to determine an appropriate basal rate if using aninsulin pump, an appropriate injection of long acting insulin (e.g., iftreating diabetes with multiple daily injections), an appropriate CR, oran appropriate ISF. In some cases, methods and systems provided hereincan use a personalized CR to determine an appropriate insulin bolus(using a pump, pen, or syringe to deliver quick acting insulin) toaddress an amount of carbohydrates consumed for a meal. In some cases,methods and systems provided herein can use a personalized ISF todetermine an appropriate correction bolus (using a pump, pen, or syringeto deliver quick acting insulin) to address an elevated blood glucoselevel.

In some cases, methods and systems of the present disclosure may includereceiving one or more manually input therapeutic parameters for a PWDand comparing the manually input therapeutic parameters to a typicalprobability distribution. For example, the combination of therapeuticparameters may be compared to a probability distribution of a generalcombination of therapeutic parameters or a distribution of therapeuticparameters of a large diabetic population. After such a comparison hasbeen performed, the result of such a comparison may be presented to auser (such as the PWD or a caregiver of the PWD). Additionally oralternatively, a recommended modification to the therapeutic parametersmay be provided to the user.

In some cases, methods and systems of the present disclosure may includedisabling personalization or certain aspects of personalization if a PWDreceives a bolus dose or if the PWD receives a bolus dose different thana recommended bolus dose of insulin. For example, a user may request abolus dose of insulin based on an upcoming meal or a high blood glucoselevel. A control device may provide a recommended bolus dose based onone or more therapeutic parameters of the PWD. If the user overrides therecommended bolus dose to deliver more or less insulin than recommended,the control device may disable any personalization for a certain amountof time after the user override such that the personalization is notbased on a bolus dose that is too large or too small. Such a lockoutfeature may prevent the control device from delivering a varying ratioof the baseline basal rate (e.g., prevent the control device fromdelivering 0×, 1×, or 2× the baseline basal rate), and/or may preventthe control device from considering the blood glucose levels whileaffected by the overridden bolus dose in personalizing the baselinebasal rate for diurnal time periods.

Example Diabetes Management System

FIG. 1 depicts an example diabetes management system 10, in accordancewith one or more embodiments of the present disclosure. The diabetesmanagement system 10 may include a pump assembly 15 for insulin and acontinuous glucose monitor 50. As shown, the continuous glucose monitor50 is in wireless communication with pump assembly 15. In some cases, acontinuous glucose monitor can be in wired communication with pumpassembly 15. In some cases, not shown, a continuous glucose monitor canbe incorporated into an insulin pump assembly. As shown, pump assembly15 can include a reusable pump controller 200 that forms part of thepump assembly 15. In some cases, reusable pump controller 200 is adaptedto determine one or more basal delivery rates. In some cases, continuousglucose monitor 50 can act as a controller adapted to communicate basaldelivery rates to pump assembly 15.

Pump assembly 15, as shown, can include reusable pump controller 200 anda disposable pump 100, which can contain a reservoir for retaininginsulin. A drive system for pushing insulin out of the reservoir can beincluded in either the disposable pump 100 or the reusable pumpcontroller 200 in a controller housing 210. Reusable pump controller 200can include a wireless communication device 247, which can be adapted tocommunicate with a wireless communication device 54 of continuousglucose monitor 50 and other diabetes devices in the system, such asthose discussed below. In some cases, pump assembly 15 can be sized tofit within a palm of a hand 5. Pump assembly 15 can include an infusionset 146. Infusion set 146 can include a flexible tube 147 that extendsfrom the disposable pump 100 to a subcutaneous cannula 149 that may beretained by a skin adhesive patch (not shown) that secures thesubcutaneous cannula 149 to the infusion site. The skin adhesive patchcan retain the subcutaneous cannula 149 in fluid communication with thetissue or vasculature of the PWD so that the medicine dispensed throughtube 147 passes through the subcutaneous cannula 149 and into the PWD'sbody. A cap device 130 can provide fluid communication between an outputend of an insulin cartridge (not shown) and tube 147 of infusion set146. Although pump assembly 15 is depicted as a two-part insulin pump,one piece insulin pumps are also contemplated. Additionally, insulinpump assemblies used in methods and systems provided herein canalternatively be a patch pump.

Continuous glucose monitor 50 (e.g., a glucose sensor) can include ahousing 52, a wireless communication device 54, and a sensor shaft 56.The wireless communication device 54 can be contained within the housing52 and the sensor shaft 56 can extend outward from the housing 52. Inuse, the sensor shaft 56 can penetrate the skin 20 of a user to makemeasurements indicative of the PWD's blood glucose level or the like. Insome cases, the sensor shaft 56 can measure glucose or another analytein interstitial fluid or in another fluid and correlate that to bloodglucose levels. In response to the measurements made by the sensor shaft56, the continuous glucose monitor 50 can employ the wirelesscommunication device 54 to transmit data to a corresponding wirelesscommunication device 247 housed in the pump assembly 15. In some cases,the continuous glucose monitor 50 may include a circuit that permitssensor signals (e.g., data from the sensor shaft 56) to be communicatedto the wireless communication device 54. The wireless communicationdevice 54 can transfer the collected data to reusable pump controller200 (e.g., by wireless communication to the wireless communicationdevice 247). Additionally or alternatively, the system 10 may includeanother glucose monitoring device that may utilize any of a variety ofmethods of obtaining information indicative of a PWD's blood glucoselevels and transferring that information to reusable pump controller200. For example, an alternative monitoring device may employ amicropore system in which a laser porator creates tiny holes in theuppermost layer of a PWD's skin, through which interstitial glucose ismeasured using a patch. In the alternative, the monitoring device canuse iontophoretic methods to non-invasively extract interstitial glucosefor measurement. In other examples, the monitoring device can includenon-invasive detection systems that employ near IR, ultrasound orspectroscopy, and particular implementations of glucose-sensing contactlenses. In other examples, the monitoring device can include detectglucose levels using equilibrium fluorescence detectors (e.g., sensorsincluding a diboronic acid receptor attached to a fluorophore).Furthermore, it should be understood that in some alternativeimplementations, continuous glucose monitor 50 can be in communicationwith reusable pump controller 200 or another computing device via awired connection. In some cases, continuous glucose monitor 50 can beadapted to provide blood glucose measurements for a PWD when in use forthe PWD at regular or irregular time intervals. In some cases,continuous glucose monitor 50 can detect blood glucose measurements atleast every thirty minutes, at least every fifteen minutes, at leastevery ten minutes, at least every five minutes, or about every minute.In some cases, continuous glucose monitor 50 can itself determine abasal delivery rate using methods provided herein and communicate thatbasal rate to the pump assembly 15. In some cases, continuous glucosemonitor 50 can transmit blood glucose measurement data to reusable pumpcontroller 200 and reusable pump controller 200 can use methods providedherein to determine a basal delivery rate. In some cases, a remotecontroller can receive glucose data from continuous glucose monitor 50,determine a basal delivery rate using methods provided herein, andcommunicate the basal rate to pump assembly 15.

Diabetes management system 10 may optionally include a blood glucosemeter 70 (e.g., a glucose sensor). In some cases, blood glucose meter 70can be in wireless communication with reusable pump controller 200.Blood glucose meter 70 can take a blood glucose measurement using one ormore test strips (e.g., blood test strips). A test strip can be insertedinto a strip reader portion of the blood glucose meter 70 and thenreceive the PWD's blood to determine a blood glucose level for the PWD.In some cases, the blood glucose meter 70 is configured to analyze thecharacteristics of the PWD's blood and communicate (e.g., via aBLUETOOTH® wireless communication connection) the information toreusable pump controller 200. In some cases, a user can manually input aglucose meter reading. The blood glucose meter 70 can be manuallyoperated by a user and may include an output subsystem (e.g., display,speaker) that can provide the user with blood glucose readings that canbe subsequently entered into the controller or user interface to collectthe data from an unconnected BGM into the system. The blood glucosemeter 70 may be configured to communicate data (e.g., blood glucosereadings) obtained to reusable pump controller 200 and/or other devices,such as a mobile computing device 60 (e.g., a control device). Suchcommunication can be over a wired and/or wireless connection, and thedata can be used by system 10 for a number of functions (e.g.,calibrating the continuous glucose monitor 50, confirming a reading fromthe continuous glucose monitor 50, determining a more accurate bloodglucose reading for a bolus calculation, detecting a blood glucose levelwhen the continuous glucose monitor 50 is malfunctioning).

In some cases, the system 10 can further include a mobile computingdevice 60 that can communicate with the reusable pump controller 200through a wireless and/or wired connection with the reusable pumpcontroller 200 (e.g., via a BLUETOOTH® wireless communication connectionor a near-field communication connection). In some cases, the mobilecomputing device 60 communicates wirelessly with other diabetes devicesof system 10. The mobile computing device 60 can be any of a variety ofappropriate computing devices, such as a smartphone, a tablet computingdevice, a wearable computing device, a smartwatch, a fitness tracker, alaptop computer, a desktop computer, and/or other appropriate computingdevices. In some cases (for example, where the reusable pump controller200 does not determine a basal delivery rate), the mobile computingdevice 60 can receive and log data from other elements of the system 10and determine basal delivery rates using methods provided herein. Insome cases, a user can input relevant data into the mobile computingdevice 60. In some cases, the mobile computing device 60 can be used totransfer data from the reusable pump controller 200 to another computingdevice (e.g., a back-end server or cloud-based device). In some cases,one or more methods provided herein can be performed or partiallyperformed by the other computing device. In some cases, the mobilecomputing device 60 provides a user interface (e.g., graphical userinterface (GUI), speech-based user interface, motion-controlled userinterface) through which users can provide information to controloperation of the reusable pump controller 200 and the system 10. Forexample, the mobile computing device 60 can be a mobile computing devicerunning a mobile app that communicates with reusable pump controller 200over short-range wireless connections (e.g., BLUETOOTH® connection,Wi-Fi Direct connection, near-field communication connection, etc.) toprovide status information for the system 10 and allow a user to controloperation of the system 10 (e.g., toggle between delivery modes, adjustsettings, log food intake, change a fear of hypoglycemia index (FHI),confirm/modify/cancel bolus dosages, and the like).

Optionally, system 10 may include a bolus administering device 80 (e.g.,a syringe, an insulin pen, a smart syringe with device communicationcapabilities, or the like) through which bolus dosages can be manuallyadministered to a PWD. In some cases, a suggested dosage for a bolus tobe administered using the bolus administering device 80 can be output toa user via the user interface of reusable pump controller 200 and/or theuser interface of the mobile computing device 60. In some cases, thebolus administering device 80 can communicate through a wired and/orwireless connection with reusable pump controller 200 and/or the mobilecomputing device 60. In some cases, system 10 can allow users to inputinsulin deliveries made using a syringe or insulin pen.

In some cases, methods and systems of treating diabetes can include theuse of an insulin pump assembly 15, which can be used to deliver both acontinuous (or semi-continuous) supply of quick acting insulin at apersonalized BR and to delivery boluses of the quick acting insulin tomake corrections for elevated blood glucose levels or to addressconsumed carbohydrates. In some cases, methods and systems of treatingdiabetes can include the use of a continuous glucose monitor 50 (CGM)that can communicate blood glucose data to a controller such as themobile computing device 60 and/or the reusable pump controller 200 thatcan automate insulin delivery dynamically to address current oranticipated high or low blood glucose levels. In some cases, methods andsystems provided herein can make adjustments to therapeutic parametersbased upon the automated insulin deliveries determined using continuous(or semi-continuous) blood glucose data.

In some cases, methods and systems of treating diabetes can include theuse of multiple daily injections (MDIs) of different types of insulin.For example, MDIs can include the injection of long acting insulin atleast once a day to cover a baseline insulin requirement and theinjection of a quick acting insulin to make corrections or to addressconsumed carbohydrates.

In some embodiments, the system 10 may include a bolus administeringdevice 80 (e.g., a syringe, an insulin pen, a smart syringe with devicecommunication capabilities using quick acting insulin, or the like)through which bolus dosages can be manually administered to a PWD. Asillustrated in FIG. 1, such a bolus administering device may be referredto as an injection based delivery device. In some cases, a suggesteddosage for a bolus to be administered using the bolus administeringdevice 80 can be output to a user via the user interface of reusablepump controller 200 and/or the user interface of the mobile computingdevice 60. In some cases, the bolus administering device 80 cancommunicate through a wired and/or wireless connection with reusablepump controller 200 and/or the mobile computing device 60. In somecases, system 10 can allow users to input insulin deliveries made usinga syringe or insulin pen.

In some embodiments, the system 10 may include a basal administeringdevice 82 (e.g., a syringe, an insulin pen, a smart syringe with devicecommunication capabilities using long acting insulin, or the like)through which basal insulin can be manually administered to a PWD, suchas a once per day dose or a twice per day dose. As illustrated in FIG.1, such a basal administering device may be referred to as an injectionbased delivery device. In some cases, the amount of basal insulin for agiven dose may be determined by the mobile computing device 60. Forexample, the mobile computing device 60 may display an amount of insulinto be delivered as a daily basal dose. Additionally or alternatively, ifthe basal administering device 82 includes communication capabilities,the mobile computing device 60 may transmit a message to the basaladministering device 82 indicating the amount of basal insulin to bedelivered in the dose.

Additional Details about Example Pump Assembly

FIGS. 2A and 2B illustrate additional details of the example system 10of FIG. 1.

FIGS. 2A and 2B provide additional details about example pump assembly15 as discussed above in regards to FIG. 1. FIG. 2B depicts the detailsof example reusable pump controller 200.

Referring now to FIG. 2A, disposable pump 100 in this embodimentincludes a pump housing structure 110 that defines a cavity 116 in whicha fluid cartridge 120 can be received. Disposable pump 100 also caninclude a cap device 130 to retain the fluid cartridge 120 in the cavity116 of the pump housing structure 110. Disposable pump 100 can include adrive system (e.g., including a battery powered actuator, a gear system,a drive rod, and other items that are not shown in FIG. 2A) thatadvances a plunger 125 in the fluid cartridge 120 so as to dispensefluid therefrom. In this embodiment, reusable pump controller 200communicates with disposable pump 100 to control the operation of thedrive system. For example, in some cases, the reusable pump controller200 can generate a message for the disposable pump 100 directing thedisposable pump 100 to deliver a certain amount of insulin or deliverinsulin at a certain rate. In some cases, such a message may direct thedisposable pump 100 to advance the plunger 125 a certain distance. Insome cases, not depicted, reusable pump controller 200 may include auser interface to control the operation of disposable pump 100. In somecases, disposable pump 100 can be disposed of after a single use. Forexample, disposable pump 100 can be a “one-time-use” component that isthrown away after the fluid cartridge 120 therein is exhausted.Thereafter, the user can removably attach a new disposable pump 100(having a new fluid cartridge) to the reusable pump controller 200 forthe dispensation of fluid from a new fluid cartridge. Accordingly, theuser is permitted to reuse reusable pump controller 200 (which mayinclude complex or valuable electronics, as well as a rechargeablebattery) while disposing of the relatively low-cost disposable pump 100after each use. Such a pump assembly 15 can provide enhanced user safetyas a new pump device (and drive system therein) is employed with eachnew fluid cartridge.

The pump assembly 15 can be a medical infusion pump assembly that isconfigured to controllably dispense a medicine from the fluid cartridge120. As such, the fluid cartridge 120 can contain a medicine 126 to beinfused into the tissue or vasculature of a targeted individual, such asa human or animal patient. For example, disposable pump 100 can beadapted to receive a fluid cartridge 120 in the form of a carpule thatis preloaded with insulin or another medicine for use in the treatmentof diabetes (e.g., Exenatide (BYETTA®, BYDUREON®) and liraglutide(VICTOZA®, SYMLIN®, or others). Such a fluid cartridge 120 may besupplied, for example, by Eli Lilly and Co. of Indianapolis, Ind. Thefluid cartridge 120 may have other configurations. For example, thefluid cartridge 120 may comprise a reservoir that is integral with thepump housing structure 110 (e.g., the fluid cartridge 120 can be definedby one or more walls of the pump housing structure 110 that surround aplunger to define a reservoir in which the medicine is injected orotherwise received).

In some embodiments, disposable pump 100 can include one or morestructures that interfere with the removal of the fluid cartridge 120after the fluid cartridge 120 is inserted into the cavity 116. Forexample, the pump housing structure 110 can include one or more retainerwings (not shown) that at least partially extend into the cavity 116 toengage a portion of the fluid cartridge 120 when the fluid cartridge 120is installed therein. Such a configuration may facilitate the“one-time-use” feature of disposable pump 100. In some embodiments, theretainer wings can interfere with attempts to remove the fluid cartridge120 from disposable pump 100, thus ensuring that disposable pump 100will be discarded along with the fluid cartridge 120 after the fluidcartridge 120 is emptied, expired, or otherwise exhausted. In anotherexample, the cap device 130 can be configured to irreversibly attach tothe pump housing structure 110 so as to cover the opening of the cavity116. For example, a head structure of the cap device 130 can beconfigured to turn so as to threadably engage the cap device 130 with amating structure along an inner wall of the cavity 116, but the headstructure may prevent the cap device from turning in the reversedirection so as to disengage the threads. Accordingly, disposable pump100 can operate in a tamper-resistant and safe manner because disposablepump 100 can be designed with a predetermined life expectancy (e.g., the“one-time-use” feature in which the pump device is discarded after thefluid cartridge 120 is emptied, expired, or otherwise exhausted).

Still referring to FIG. 2A, reusable pump controller 200 can beremovably attached to disposable pump 100 so that the two components aremechanically mounted to one another in a fixed relationship. In someembodiments, such a mechanical mounting can also form an electricalconnection between the reusable pump controller 200 and disposable pump100 (for example, at electrical connector 118 of disposable pump 100).For example, reusable pump controller 200 can be in electricalcommunication with a portion of the drive system (not shown) ofdisposable pump 100. In some embodiments, disposable pump 100 caninclude a drive system that causes controlled dispensation of themedicine or other fluid from the fluid cartridge 120. In someembodiments, the drive system incrementally advances a piston rod (notshown) longitudinally into the fluid cartridge 120 so that the fluid isforced out of an output end 122. A septum 121 at the output end 122 ofthe fluid cartridge 120 can be pierced to permit fluid outflow when thecap device 130 is connected to the pump housing structure 110. Forexample, the cap device 130 may include a penetration needle thatpunctures the septum 121 during attachment of the cap device 130 to thepump housing structure 110. Thus, when disposable pump 100 and reusablepump controller 200 are mechanically attached and thereby electricallyconnected, reusable pump controller 200 communicates electronic controlsignals via a hardwire-connection (e.g., electrical contacts alongelectrical connector 118 or the like) to the drive system or othercomponents of disposable pump 100. In response to the electrical controlsignals from reusable pump controller 200, the drive system ofdisposable pump 100 causes medicine to incrementally dispense from thefluid cartridge 120. Power signals, such as signals from a battery (notshown) of reusable pump controller 200 and from the power source (notshown) of disposable pump 100, may also be passed between reusable pumpcontroller 200 and disposable pump 100.

Referring again to FIGS. 1 and 2A the pump assembly 15 can be configuredto be portable and can be wearable and concealable. For example, a PWDcan conveniently wear the pump assembly 15 on the PWD's skin (e.g., skinadhesive) underneath the PWD's clothing or carry disposable pump 100 inthe PWD's pocket (or other portable location) while receiving themedicine dispensed from disposable pump 100. The pump assembly 15 isdepicted in FIG. 1 as being held in a PWD's hand 5 so as to illustratethe size of the pump assembly 15 in accordance with some embodiments.This embodiment of the pump assembly 15 is compact so that the PWD canwear the pump assembly 15 (e.g., in the PWD's pocket, connected to abelt clip, adhered to the PWD's skin, or the like) without the need forcarrying and operating a separate module. In such embodiments, the capdevice 130 of disposable pump 100 can be configured to mate with aninfusion set 146. In general, the infusion set 146 can be a tubingsystem that connects the pump assembly 15 to the tissue or vasculatureof the PWD (e.g., to deliver medicine into the tissue or vasculatureunder the PWD's skin). The infusion set 146 can include a tube 147 thatis flexible and that extends from disposable pump 100 to a subcutaneouscannula 149 that may be retained by a skin adhesive patch (not shown)that secures the subcutaneous cannula 149 to the infusion site. The skinadhesive patch can retain the subcutaneous cannula 149 in fluidcommunication with the tissue or vasculature of the PWD so that themedicine dispensed through the tube 147 passes through the subcutaneouscannula 149 and into the PWD's body. The cap device 130 can providefluid communication between the output end 122 (FIG. 2A) of the fluidcartridge 120 and the tube 147 of the infusion set 146.

In some embodiments, the pump assembly 15 can be pocket-sized so thatdisposable pump 100 and reusable pump controller 200 can be worn in thePWD's pocket or in another portion of the PWD's clothing. In somecircumstances, the PWD may desire to wear the pump assembly 15 in a morediscrete manner. Accordingly, the PWD can pass the tube 147 from thepocket, under the PWD's clothing, and to the infusion site where theadhesive patch can be positioned. As such, the pump assembly 15 can beused to deliver medicine to the tissues or vasculature of the PWD in aportable, concealable, and discrete manner.

In some embodiments, the pump assembly 15 can be configured to adhere tothe PWD's skin directly at the location in which the skin is penetratedfor medicine infusion. For example, a rear surface of disposable pump100 can include a skin adhesive patch so that disposable pump 100 can bephysically adhered to the skin of the PWD at a particular location. Inthese embodiments, the cap device 130 can have a configuration in whichmedicine passes directly from the cap device 130 into an infusion set146 that is penetrated into the PWD's skin. In some examples, the PWDcan temporarily detach reusable pump controller 200 (while disposablepump 100 remains adhered to the skin) so as to view and interact withthe user interface 220.

In some embodiments, the pump assembly 15 can operate during anautomated mode to deliver basal insulin according the methods providedherein. In some cases, pump assembly 15 can operate in an open loop modeto deliver insulin at the BBR. A basal rate of insulin can be deliveredin an incremental manner (e.g., dispense 0.10 Units every five minutesfor a rate of 1.2 Units/hour) according to a selected basal insulindelivery profile. A user can use the user interface on mobile computingdevice 60 to select one or more bolus deliveries, for example, to offsetthe blood glucose effects caused by food intake, to correct for anundesirably high blood glucose level, to correct for a rapidlyincreasing blood glucose level, or the like. In some circumstances, thebasal rate delivery pattern may remain at a substantially constant ratefor a long period of time (e.g., a first basal dispensation rate for aperiod of hours in the morning, and a second basal dispensation rate fora period of hours in the afternoon and evening). In contrast, the bolusdosages can be more frequently dispensed based on calculations made byreusable pump controller 200 or the mobile computing device 60 (whichthen communicates to reusable pump controller 200). For example,reusable pump controller 200 can determine that the PWD's blood glucoselevel is rapidly increasing (e.g., by interpreting data received fromthe continuous glucose monitor 50), and can provide an alert to the user(via the user interface 220 or via the mobile computing device 60) sothat the user can manually initiate the administration of a selectedbolus dosage of insulin to correct for the rapid increase in bloodglucose level. In one example, the user can request (via the userinterface of mobile computing device 60) a calculation of a suggestedbolus dosage (e.g., calculated at the mobile computing device 60 basedupon information received from the user and from reusable pumpcontroller 200, or alternatively calculated at reusable pump controller200 and communicated back via the mobile computing device 60 for displayto the user) based, at least in part, on a proposed meal that the PWDplans to consume.

Referring now to FIG. 2B, reusable pump controller 200 (shown in anexploded view) houses a number of components that can be reused with aseries of successive disposable pumps 100. In particular, reusable pumpcontroller 200 can include control circuitry 240 (e.g., a controldevice) and a rechargeable battery pack 245, each arranged in thecontroller housing 210. The rechargeable battery pack 245 may provideelectrical energy to components of the control circuitry 240, othercomponents of the controller device (e.g., a display device and otheruser interface components, sensors, or the like), or to components ofdisposable pump 100. The control circuitry 240 may be configured tocommunicate control or power signals to the drive system of disposablepump 100, or to receive power or feedback signals from disposable pump100.

The control circuitry 240 of reusable pump controller 200 can includeone or more microprocessors 241 configured to execute computer-readableinstructions stored on one or more memory devices 242 so as to achieveany of the control operations described herein. At least one memorydevice 242 of the control circuitry 240 may be configured to store anumber of user-specific dosage parameters. One or more user-specificdosage parameters may be input by a user via the user interface 220.Further, as described below in connection with FIG. 2A, varioususer-specific dosage parameters can be automatically determined and/orupdated by control operations implemented by the control circuitry 240of reusable pump controller 200. For example, the control circuitry 240can implement a secondary feedback loop to determine and/or update oneor more user-specific dosage parameters in parallel with the infusionpump system 10 operating in a closed-loop delivery mode. Whetherdetermined automatically or received via the mobile computing device 60(or via the user interface 220 of reusable pump controller 200), thecontrol circuitry 240 can cause the memory device 242 to store theuser-specific dosage parameters for future use during operationsaccording to multiple delivery modes, such as closed-loop and open-loopdelivery modes. Additionally, the control circuitry 240 can causereusable pump controller 200 to periodically communicate theuser-specific dosage parameters to the mobile computing device 60 forfuture use during operations by the mobile computing device 60 or forsubsequent communication to a cloud-based computer network.

Such user-specific dosage parameters may include, but are not limitedto, one or more of the following: total daily basal dosage limits (e.g.,in a maximum number of units/day), various other periodic basal dosagelimits (e.g., maximum basal dosage/hour, maximum basal dosage/six hourperiod), insulin sensitivity (e.g., in units of mg/dL/insulin unit),carbohydrate ratio (e.g., in units of g/insulin unit), insulin onsettime (e.g., in units of minutes and/or seconds), insulin on boardduration (e.g., in units of minutes and/or seconds), and basal rateprofile (e.g., an average basal rate or one or more segments of a basalrate profile expressed in units of insulin unit/hour). Also, the controlcircuitry 240 can cause the memory device 242 to store (and can causereusable pump controller 200 to periodically communicate out to themobile computing device 60) any of the following parameters derived fromthe historical pump usage information: dosage logs, average total dailydose, average total basal dose per day, average total bolus dose perday, a ratio of correction bolus amount per day to food bolus amount perday, amount of correction boluses per day, a ratio of a correction bolusamount per day to the average total daily dose, a ratio of the averagetotal basal dose to the average total bolus dose, average maximum bolusper day, and a frequency of cannula and tube primes per day. To theextent these aforementioned dosage parameters or historical parametersare not stored in the memory device 242, the control circuitry 240 canbe configured to calculate any of these aforementioned dosage parametersor historical parameters from other data stored in the memory device 242or otherwise input via communication with the mobile computing device60.

Additionally or alternatively, an indicator light 230 may beillustrated. The indicator light 230 may include one or more lights oricons that can indicate one or more pieces of information relative tothe operation of the reusable pump controller 200. For example, theindicator light 230 may indicate whether the reusable pump controller200 is operating in a mode in which it is adjusting or modifying insulindelivery, or is delivering insulin according to a preprogrammedschedule. Additionally or alternatively, the indicator lights 230 mayindicate that a user has a message, or that the disposable pump 100 isout of insulin, or the like.

Modifications, additions, or omissions may be made to the system 10without departing from the scope of the present disclosure. For example,the system 10 may have more or fewer elements than those illustrated ordescribed in the present disclosure. Additionally, the system 10 mayinclude any of the components or arrangements consistent with thepresent disclosure. For example, the system 10 may be implementedwithout the use of the pump assembly 15 and instead be implemented withthe basal administering device 82 and the bolus administering device 80.As another example, the system 10 may be implemented with the bloodglucose meter (BGM) 70 and the continuous glucose monitor (CGM) 50 maybe omitted.

Relationship Between BR, ISF, and CR

Methods and systems provided herein can use predetermined relationshipsbetween therapeutic parameters. For example, methods and systemsprovided herein can utilize a general probabilistic relationship betweenBR, CR, and/or ISF to determine what one or more therapeutic parametersare when starting with another therapeutic parameter.

In some cases, methods and systems provided herein can estimate aninitial ISF and CR based on an initial BR. For example, it may be that aPWD or their caregivers might know a total daily basal (TDB) amount, butnot know the CR or ISF of the PWD. In some cases, an initial ISF settingcan be set by the following equation:ISF=x*BR^(−y)  Equation (1)where BR is the total amount of number of units of basal insulin (ortotal number of units of long acting insulin) per day, x is a numberbetween 1115 and 1140, and y is a number between 1.00 and 1.06. In somecases, x can be a number between 1120 and 1135 and y can be a numberbetween 1.03 and 1.06. In some cases, x can be a number between 1125 and1135 and y can be a number of about 1.05. In some cases, an ISFcalculation provided above can be rounded to the nearest tenth of aninteger or the nearest integer.

FIG. 3A illustrates an example set of curves 300 a illustratingpotential ISFs based on a BR, in accordance with one or more embodimentsof the present disclosure. Each of the curves 310, 320, and 330 mayillustrate an alternative approach to determining a relationship betweenISF and BR.

The first curve 310 illustrates an embodiment in which ISF is based onEquation 1 above, with x equal to 1140 and y equal to 1.00.

In some embodiments, one or more values used in determining ISF from BRmay be rounded to a certain number of significant digits. For example,as illustrated in FIG. 3A, the second curve 320 illustrates anembodiment in which the ISF may be rounded to a whole number. Forexample, the second curve 320 is based on Equation 1, with x equal to1127 and y equal to 1.05.

The third curve 330 illustrates an embodiment in which ISF is based onEquation 1, with x equal to 1115 and y equal to 1.06.

The fourth curve 340 illustrates the rule of 1800 as a reference forcomparison to the first, second, and third curves 310, 320, and 330. Therule of 1800 is, for quick acting insulin, a PWD's ISF is determined bydividing 1800 by the total daily insulin dose, including basal insulinand bolus insulin. For example, if a PWD had basal insulin of 30 unitsof insulin and typically have boluses of 30 units per day, the PWD's ISFwould be 1800/(30+30)=50.

In some embodiments, one or more curves comparable or similar to thoseillustrated in FIG. 3A may be stored in a control device (such as themobile computing device 60 of FIG. 1) such that the one or more of thecurves may function as a look-up table. For example, a user may input aBR and a corresponding ISF may be observed along the curve.

In some cases, an initial CR setting can be set by the followingequation:CR=a*BR^(−b)  Equation (2)where BR is the total amount of number of units of basal insulin (ortotal number of units of long acting insulin) per day, a is a numberbetween 114 and 126, and b is a number between 0.785 and 0.815. In somecases, a can be a number between 117 and 123 and b can be a numberbetween 0.79 and 0.81. In some cases, a can be a number between 119 and121 and b can be a number of about 0.8. In some cases, a CR calculationprovided above can be rounded to the nearest tenth of an integer or thenearest integer.

FIG. 3B illustrates an example set of curves 300 b illustratingpotential CRs based on a BR, in accordance with one or more embodimentsof the present disclosure. Each of the curves 360, 370, and 380 mayillustrate an alternative approach to determining a relationship betweenCR and BR.

The first curve 360 illustrates an embodiment in which CR is based onEquation 2 above, with a equal to 126 and b equal to 0.785.

In some embodiments, one or more values used in determining ISF from BRmay be rounded to a certain number of significant digits. For example,as illustrated in FIG. 3B, the second curve 370 illustrates anembodiment in which the CR may be rounded to a whole number. Forexample, the second curve 370 is based on Equation 2, with a equal to120 and b equal to 0.8.

The third curve 380 illustrates an embodiment in which CR is based onEquation 2, with a equal to 114 and b equal to 0.815.

The fourth curve 350 illustrates the rule of 500 as a reference forcomparison to the first, second, and third curves 360, 370, and 380. Therule of 500 is, for quick acting insulin, a PWD's CR is determined bydividing 500 by the total daily insulin dose, including basal insulinand bolus insulin. For example, if a PWD had basal insulin of 30 unitsof insulin and typically have boluses of 30 units per day, the PWD's ISFwould be 500/(30+30)=8.33.

In some embodiments, one or more curves comparable or similar to thoseillustrated in FIG. 3B may be stored in a control device (such as themobile computing device 60 of FIG. 1) such that the one or more of thecurves may function as a look-up table. For example, a user may input aBR and a corresponding CR may be observed along the curve.

Modifications, additions, or omissions may be made to the sets of curves300 a and 300 b without departing from the scope of the presentdisclosure. For example, the sets of curves 300 a and 300 b may havemore or fewer elements than those illustrated or described in thepresent disclosure. For example, the sets of curves 300 a and 300 b mayinclude any number of curves as multiple approaches to determiningtherapeutic parameters.

Variation from General Probability

In some cases, a user (e.g., a PWD or caregiver) may know, or believethey know, their personalized BR, CR, and ISF (or two out of the three).Methods and systems provided herein, however, can store a probabilitydistribution for relationships between BR, CR, and ISF for the generalpopulation. In some cases, methods and systems provided herein can alerta user to the presence of a mismatch between entered BR, CR, and ISFvalues (e.g., based on a distance between the entered therapeutic valuesand the general probability distributions exceeding a threshold) andprovide a recommendation of an adjusted CR, ISF, BR, or combinationthereof based on the probability distribution. For example, therecommended CR, ISF, BR, or combinations thereof may move thecombination of therapeutic parameters such that the combination iscloser to the general probability distribution of parameters.

FIGS. 4A and 4B illustrate an example visualization 400 of a probabilitydistribution of a general BR, general CR, and general ISF, in accordancewith one or more embodiments of the present disclosure. As illustratedin FIGS. 4A and 4B, the probability distribution includes a generallyellipsoidal distribution with a major axis 410, and first minor axis 420and second minor axis 430. The distribution also has a midpoint 450. Thecloser a combination of BR, CR, and ISF are to the midpoint 450, thecloser the combination is to the middle of the distribution. Inparticular, the visualization 400 of FIGS. 4A and 4B represents amultivariate normal distribution of the logarithms of BR, CR, and ISF.

The multivariate normal distribution illustrated in FIGS. 4A and 4B mayinclude a mean μ that is approximately

$\mu = \begin{bmatrix}3.0111 \\2.3757 \\3.8645\end{bmatrix}$and a covariation matrix Σ that is approximately

$\sum{= {\begin{bmatrix}0.2843 & {- 0.1657} & {- 0.2216} \\{- 0.1657} & 0.1978 & 0.1863 \\{- 0.2216} & 0.1863 & 0.2968\end{bmatrix}.}}$

In some embodiments, a distance of a combination of BR, CR, and ISF fromthe probability distribution may be determined mathematically. Forexample, the Mahalanobis Distance, D_(m) of an observed relationship ofBR, CR, and ISF (e.g., the combination of entered therapeutic values asa matrix) may be determined by:D _(m)=√{square root over ((x−μ)^(T)Σ⁻¹(x−μ))}  Equation (3)The probability accumulated in the region bounded by the ellipsoid ofFIGS. 4A and 4B with Mahalanobis Distance D_(m) may be determined by:

$\begin{matrix}{{P\left( {{{x - \mu}}_{\sum} \leq D_{m}} \right)} = \frac{\gamma\left( {\frac{3}{2^{\prime}}\frac{D_{m}^{2}}{2}} \right)}{\Gamma\left( \frac{3}{2} \right)}} & {{Equation}\mspace{14mu}(4)}\end{matrix}$where 3 is used as the number of dimensions (e.g., the values of BR, CR,and ISF),

$\gamma\left( {\frac{3}{2^{\prime}}\frac{D_{m}^{2}}{2}} \right)$is the lower incomplete Gamma Function, and Γ(3/2) is the GammaFunction. The result of Equation (4) may yield a number between 0 (closeto the midpoint 450) and 1 (far from the midpoint 450).

Using the approach above, the following example determines how uncommonthe relationship between BR, CR, and ISF is if a user enters BR=16,CR=12, and ISF=130.

$\mspace{20mu}{x = {{\ln\begin{matrix}\left\lbrack 16 \right. & 12 & \left. 130 \right\rbrack\end{matrix}^{T}} = \begin{matrix}\left\lbrack 2.77 \right. & 2.48 & \left. 4.87 \right\rbrack\end{matrix}^{T}}}$$D_{m}^{2} = {{\left( {x - \mu} \right)^{T}{\sum^{- 1}\left( {x - \mu} \right)}} = {{{\begin{bmatrix}{- 0.2384} \\0.1092 \\1.0030\end{bmatrix}^{T}\begin{bmatrix}9.0831 & 2.9883 & 4.9060 \\2.9883 & 13.3501 & {- 6.1487} \\4.9060 & {- 6.1487} & 10.8917\end{bmatrix}}\begin{bmatrix}{- 0.2384} \\0.1092 \\1.0030\end{bmatrix}} = 7.78}}$$\mspace{20mu}{{P\left( {{{x - \mu}}_{\sum} \leq D_{m}} \right)} = {\frac{\gamma\left( {\frac{3}{2^{\prime}}\frac{D_{m}^{2}}{2}} \right)}{\Gamma\left( \frac{3}{2} \right)} = {\frac{\gamma\left( {\frac{3}{2^{\prime}}\frac{7.78}{2}} \right)}{\Gamma\left( \frac{3}{2} \right)} = {\frac{0.8412}{0.8862} = 0.949}}}}$  (1 − 0.949) × 100% = 5.1%Thus, with the combination of therapeutic parameters of BR=16, CR=12,and ISF=130, only 5.1% of the population lies further from the midpoint450 of the ellipsoid of FIGS. 4A and 4B.

Additionally or alternatively, in some embodiments a determination maybe made as to how far from the major axis 410 a particular combinationof therapeutic parameters falls. For example, a determination may bemade as to the closest point on the major axis 410 from the particularcombination of therapeutic parameters. A Mahalanobis Distance from theclosest point on the major axis 410 to the midpoint 450 of the ellipsoidmay be determined, yielding a value between 0 (at the midpoint 450) and1 (very far from the midpoint 450). A numerical value of the distancefrom the major axis may be determined by Equation (5):PrDist_(MajAx)=1−((1−Distance to midpoint)/(1−Distance to major axis))  Equation (5)where PrDist_(MajAx) is a numerical value between 0 (close to the majoraxis 410) and 1 (far from the major axis 410), Distance to midpoint isthe result from Equation (4), and Distance to major axis is theMahalanobis Distance from the closest point on the major axis 410 to themidpoint 450 of the ellipsoid.

FIG. 5 illustrates example visualizations 500 of probabilitydistributions 510 of CR and ISF for a given BR. For example, thevisualizations 500 may represent slices of the ellipsoid of FIGS. 4A and4B at various values of BR.

The visualization 500 a illustrates a visualization of a probabilitydistribution 510 a of CR and ISF for a BR of 8 Units/day. For example,for a BR of 8 Units/day, the highest probability of ISF/CR isapproximately 75-100/15-20. However, the probability of these values isstill low as compared to other values of BR, such as the visualization500 g of a BR of 19 Units/day with a much higher probability with ISF/CRof approximately 40-65/9-13.

In some embodiments, the visualizations 500 a-500 p may include colorgradations or other methods to illustrate variations in probability.Additionally or alternatively, the probability distributions 510 a-510 pmay include one or more bands of one or more thresholds ofprobabilities. For example, the visualization 500 g may include fourconcentric bands of probabilities. For the given BR of 19 Units/day, asthe combination of ISF/CR associated with the given BR moves closer tothe center of the concentric band, the closer the combination comes tothe most probable combinations of therapeutic parameters.

In some cases, methods and systems provided herein can display avisualization showing a probability distribution of ISFs and CRs for anentered BR with concentric bands of constant probability. Additionallyor alternatively, the visualization may include an indicator of theinput combination of ISF and/or CR. Additionally or alternatively, asuggestion can be given to the user that the ISF and/or CR be adjustedto place the PWD's therapeutic parameters at a position on thevisualization closer to the increased probability of the generaldistribution.

FIG. 6 illustrates an example view of various user interfaces that mayfacilitate entering one or more therapeutic parameters, in accordancewith one or more embodiments of the present disclosure. The various userinterfaces may be used to edit, input, and/or verify various therapeuticparameters.

As illustrated in user interfaces 602 and 604, a user may select a menuoption to edit one or more therapeutic parameters associated with adiabetes management system (such as the diabetes management system 10 ofFIG. 1). For example, the user interface 602 may illustrate an option toedit various parameters of a diabetes management system and with theuser interface 604, a user may select one or more therapeutic parametersto modify or edit.

As illustrated in the user interfaces 606, 608, and 610, a user maymanually input numerical values for one or more therapeutic parameters.For example, via the user interface 606, a user may input a CR, via theuser interface 608, a user may input an ISF, and via the user interface610, a user may input a BR.

As illustrated in the user interfaces 612, 614, and 616, a user mayverify one or more therapeutic parameters. For example, via the userinterface 612 a user may review one or more manually input therapeuticparameters, via the user interface 614, the user may inputauthenticating credentials to verify that the user is authorized tomodify therapeutic parameters, and the user interface 616 may be aconfirmation of the modified parameters.

FIG. 7 illustrates an example user interface 700 providing arecommendation of one or more therapeutic parameters, in accordance withone or more embodiments of the present disclosure. For example, the userinterface 700 includes a visualization 702 that may include aprobability distribution 710 for an entered BR. Additionally oralternatively, the visualization 702 of the user interface 700 mayinclude an indicator 720 indicating where in the probabilitydistribution 710 the entered combination falls. For example, asillustrated in the visualization 702, the indicator 720 corresponding tothe entered combination of therapeutic parameters falls toward theoutside of the outermost band of the probability distribution 710.

In some embodiments, the user interface 700 may additionally include arecommendation window 730. The recommendation window 730 may include oneor more recommendations for variations in ISF and/or CR to move thecombination of therapeutic parameters into a lower band of theprobability distribution 710. For example, the recommendation window 730may include a first recommendation 732 of ISF and/or CR to move thetherapeutic parameter combination within the next band, and a secondrecommendation 734 of ISF and/or CR to move the therapeutic parametercombination within the most central band of probabilities. In these andother embodiments, the recommendations of the recommendation window 730may be color-coded or otherwise matched to the corresponding bands ofprobability such that a visual indicator of a region or band ofprobability is associated with a recommendation.

Modifications, additions, or omissions may be made to any of thevisualizations and/or user interfaces of FIGS. 5-7. For example, anydeterminations or comparisons may be used to generate the variousvisualizations and/or user interfaces. As another example, thevisualizations may be modified such that rather than slices based on aparticular BR, the visualizations may be slices based on ISF and/or CR.As another example, any style or format of user interface may beutilized. As another example, embodiments illustrated in FIGS. 5-7 mayhave more or fewer elements than those illustrated or described in thepresent disclosure. For example, any number of the user interfaces ofFIG. 6 may be omitted. Additionally, any number of recommendations maybe provided in FIG. 7, including at the midpoint, at the edge of eachband, etc. As another example, a line from the manually enteredcombination of therapeutic parameters and the midpoint may beillustrated with a slider along the line such that a user may select anypoint between the entered therapeutic parameter combination and themidpoint. As another example, the user may be able to select any pointin the visualization 702 of FIG. 7 and be provided a numericalindication of the combination of therapeutic parameters for the selectedpoint.

Personalization Modifications

In some cases, methods and systems provided herein automate the deliveryof insulin based on continuous or semi-continuous blood glucose data andmake adjustments to one or more therapeutic parameters based on theautomation of insulin delivery. For example, if a system or methodprovided herein increases a basal rate or provides an automaticcorrection bolus, methods and systems provided herein can also increasethe BR for a subsequent day (or the BR for a subsequent day for thattime of the day). In some cases, ISF and CR can also be adjusted forfuture time periods in response to the automation of insulin delivery.

FIG. 8 illustrates an example visualization 800 of adjusting one or moretherapeutic parameters, in accordance with one or more embodiments ofthe present disclosure. The physiology line may be similar or comparableto the major axis 410 of FIGS. 4A and 4B. The point X_(n) may representa given combination of therapeutic parameters, including ISF, CR, and/orBR. In some embodiments, the personalization of therapeutic parametersfor a PWD may include a combination of adjusting with a partial steptoward the closest point along the physiology line and a partial stepalong the physiology line, yielding a new set of therapeutic parametersX_(n+1). For example, methods and systems of the present disclosure maydetermine that after a given day the BR should be adjusted based onrepeatedly delivering 2× a baseline basal rate for multiple consecutivediurnal time blocks during that given day. Methods and systems of thepresent disclosure may also adjust CR and/or ISF such that thecombination of BR, CR, and ISF may move partially toward the physiologyline and partially parallel to the physiology line.

In some cases, adjustments to ISF, CR, and/or BR can be made in lockstepalong a predetermined mathematical relationship between ISF, CR, and BRestablished when those settings are entered. Such relationships may besimilar or comparable to those described in the present disclosure withrespect to the relationship between the various therapeutic parameters.In some cases, each of ISF, CR, and BR can be adjusted by a fixedpercentage up or down in response to automated insulin deliveries. Inother cases, ISF, CR, and BR can be independently adjusted with orwithout automated insulin deliveries.

In some cases, methods and systems using automated insulin delivery canpurposefully add noise to the system in order to observe a reaction tomake fine-tuned adjustments to a relationship between BR, ISF, and CR.

Modification Associated with Bolus Doses

In some cases, methods and systems provided herein can automate insulindelivery, but also provide recommended bolus insulin amounts that a usercan manually adjust. A recommended bolus insulin amount may have acorrection bolus component (calculated by dividing the current bloodglucose level minus the target blood glucose value by the ISF) and ameal bolus component (carbohydrates divided by CR).

FIGS. 9A and 9B illustrate example user interfaces for entering a bolusdose, in accordance with one or more embodiments of the presentdisclosure. For example in FIG. 9A, a user interface 900 a illustrates adata entry field where a user may enter the amount of carbohydrates thata PWD will be consuming. The user interface 900 a may include anindication 910 of the total insulin that is recommended that may accountfor both any correction bolus and/or a meal bolus. In some embodiments,the recommended dose of the indication 910 may be delivered without anyuser modification. For example, a message may be sent from the devicedisplaying the user interface 900 a to an insulin pump or an insulin pento deliver the recommended bolus dose. In some cases, the user maymanually adjust the recommended dose.

User interface 900 b of FIG. 9B illustrates an ability of the user tomanually adjust or override the recommended dose. The user interface 900b may include an indication 912 of the recommended dose that may besimilar or comparable to the indication 910 of FIG. 9A. The userinterface 900 b may additionally include a data entry feature 920 viawhich the user may manually adjust the bolus dose. In these and otherembodiments, a visual indicator (for example, “+,” “−,” or colored inred or green) may indicate whether the user is increasing or decreasingthe recommended dose. In some cases, a safeguard may prevent the userfrom adjusting the bolus dose beyond a threshold distance from therecommended dose, such as 15%, 25%, 50%, etc.

In methods and systems that automate insulin delivery, a manual changeto a bolus may typically result in an adjustment to subsequent basalinsulin deliveries. For example, a manual increase from a recommendedbolus may typically result in a reduction or stopping of the delivery ofbasal insulin and a manual decrease from a recommended bolus maytypically result in an increase in the delivery of basal insulin or theautomatic delivery of a correction bolus. In some cases, methods andsystems provided herein can lockout the personalization of BR subsequentto a bolus where the user manually changes the bolus amount for apredetermined or variable amount of time. For example, if a user were toadjust a bolus dose higher, the automated system may deliver 0× thebaseline basal rate for one or more next delivery actions according to adelivery profile as described herein. In some cases, the adjustment to aratio of the baseline basal rate may be disabled such that the baselinebasal rate is delivered for one or more delivery actions following thebolus dose. As another example, at the end of a given day one or morebaseline basal rates for diurnal time blocks during the day may beanalyzed and modified for future days as described herein (such as forthe same diurnal time block but on another day, or a diurnal time blockone or two segments earlier than the particular diurnal time block, or adiurnal time block at least 20 hours in the future). In some cases, acertain amount of time following the bolus dose may be excluded from theanalysis to determine if the baseline basal rate is to be modified. Insome cases, ISF and/or CR may be adjusted while BR may be locked out.Additionally or alternatively, ISF and/or CR may also be locked out frommodification.

In some cases, ISF and CR can be personalized in response to a usermanually adjusting a recommended bolus to align the ISF and CR to theuser's subjective expectation of how much insulin should be deliveredfor a bolus, with the amount of adjustment being in relationship to theamount of the bolus being attributable to a correction and the amount ofthe bolus being attributable to a meal.

For example, for a given PWD with a CR of 15, ISF of 50, a target bloodglucose level of 120, a current blood glucose level of 170 and anupcoming meal of 30 carbs, the recommended bolus may be determined bythe correction bolus (170-120)/50=1 Unit and the meal bolus (30/15)=2Units, or 3 Units total. If the user were to adjust the bolus dose to 4Units, the ratio of correction to meal bolus is 1:2, and theproportional adjustment to equal 4 Units would be the correction dose is1.33 Units and the meal bolus is 2.67 Units. To change the ISF and CR tomore closely align with the subjective expectation of the user, anincremental step may be taken toward the subjective expectation. Forexample, the complete subjective expectation for ISF may be 50/1.33, or37.5 and for CR may be 30/2.67 or 11.23. If the incremental step were10% toward the subjective expectation, the adjusted ISF may be 48.75 andthe adjusted CR may be 14.6 for the given diurnal time block. In someembodiments, such a modification may be adjusted and/or smoothed ascompared to adjacent diurnal time blocks as described herein withreference to FIG. 22.

In some cases, the override may be attributed entirely to one or morecomponents of a bolus dose, e.g., a meal component or a correctioncomponent. For example, assuming the recommended bolus of 3 Units,above, if the user were to adjust the bolus dose to 4 Units, the entireadditional 1 Unit would be attributed to the meal component, or 1 Unitattributed to the correction component and 3 Units attributed to themeal component. In one embodiment, the entire change to the mealcomponent may be attributed to carbohydrate intake. Various userspecific dosage parameters may be determined and/or adjusted responsiveto the assumption that the entire change to the meal component isattributed to carbohydrate intake. An amount of carbohydrate intake(typically in grams) may be calculated based on the meal component of 3Units using the equation, meal component=carbohydrates divided by CR. Inone embodiment, the amount of carbohydrates may be used to estimate thefuture blood glucose levels, and in further embodiments may be used toadjust a BR. In one embodiment, adjusting the BR may involve selectingand/or changing an insulin delivery profile for a given diurnal timeblock. Of course, one of ordinary skill in the will recognize caseswhere the meal component may not be entirely attributed to carbohydrateintake, and a determination of carbohydrate intake may be a fraction ofthe meal component, for example, a proportion attributed to a mealcomponent.

In some cases, such a lockout of changes to BR and/or change of ISF andCR in response to manual changes to a recommended bolus can prevent theautomated system from inappropriately adjusting therapeutic parametersin a hybrid closed-loop system for users who do not followrecommendations. Such a lockout and/or modification of therapeuticparameters may additionally adjust the system to more closely match thesubjective expectations of the user such that an appropriate amount ofinsulin is being delivered despite the modification by the user. In somecases, methods and systems provided herein may provide such a lockoutany time a bolus dose is delivered. Such an embodiment may provide atradeoff of customizability for ease of calculation and reduction inprocessing requirements of a determining device.

In some cases, methods and systems can provide notices to users thatconsistently upwardly or downwardly adjust recommended boluses that thesystem is seeking to personalize the recommended bolus amounts to theirindividual physiology and that a routine upward or downward adjustmentby the user to the recommended bolus may negatively impact thetherapeutic decisions made by the system. For example, methods andsystems of the present disclosure may monitor a number of times that auser has manually adjusted a recommended bolus dose and/or monitor themagnitude of an adjustment to a recommended bolus dose. If the number oftimes or magnitude of adjustment exceeds a threshold, a message may begenerated regarding the user modification. In some cases, the thresholdmay include adjusting the recommended dose just one time, or themagnitude may include any change. Messages regarding a user overridingthe recommended bolus dose may be provided to a PWD and/or to acaregiver of the PWD.

In some cases, methods and systems provided herein may notice or detectthat there was an occlusion and that insulin believed to have beendelivered was not actually delivered. In response to such undeliveredinsulin, methods and systems provided herein can change the method forpersonalizing therapeutic parameters. For example, the system may adjustthe insulin on board calculation or other metrics to accurately trackthe amount of insulin actually delivered rather than the amount ofinsulin believed to be delivered. As another example, the ISF, CR,and/or BR may be locked out from adjustment for the time periods thatwere affected by the occlusion.

In some cases, a PWD may be transitioning from an injection basedinsulin delivery system (e.g., syringes, pens, or the like) to a pumpbased insulin delivery system. In these and other cases, methods andsystems of the present disclosure may utilize a scaled back daily basalrate. For example, the scaled back daily rate may be betweenapproximately 85% and 95% of a basal rate utilized in the injectionbased system. Such an adjustment may be based on shifting from longacting insulin delivered via an injection delivery system to supply thebasal insulin, to utilizing quick acting insulin delivered via a pumpdelivery system to supply the basal insulin.

In some cases, methods and systems provided herein can personalize BR,CR, and ISF with blood glucose data taken using finger stick bloodglucose tests. For example, personalization may occur based on anynumber of finger stick blood glucose tests, such as between 1 and 10times a day.

In some cases, BR, one or more timestamped finger stick blood glucosesamples (MBG_(i)), and, optionally, bolus insulin (bolus_(j)) samplescan be taken throughout the day. If boluses are used, the bolus Insulinon Board (IOB_(bolus)) is calculated at times i based on boluses at timej. Basal Insulin on Board (IOB_(basal)) can also be calculated. The IOBmay be determined as described herein. For personalization using meanblood glucose (MBG) data only, a metric of how much of the insulin isbased on basal as opposed to bolus (w_(i)) may be utilized wherew_(i)=0.975. For personalization using MBG and insulin data, letw_(i)=IOB_(basal,i)/(IOB_(bolus,i)+IOB_(basal,i)).

At the end of a day, the physiological values BR, CR, and ISF may beupdated as follows. An assumption may be made that the physiologicalvalues for BR, CR, and ISP, lie along the “physiology line” illustratedin FIG. 8 and described by the equation:

$\begin{bmatrix}{\ln({BR})} \\{\ln({CR})} \\{\ln({ISF})}\end{bmatrix} = {{x_{\mu} + {t\; m}} = {{\begin{bmatrix}{\ln\left( µ_{BR} \right)} \\{\ln\left( µ_{CR} \right)} \\{\ln\left( µ_{ISF} \right)}\end{bmatrix} + {t\begin{bmatrix}m_{BR} \\m_{CR} \\m_{ISF}\end{bmatrix}}} = {{\begin{bmatrix}{\ln(20.3)} \\{\ln(10.8)} \\{\ln(47.7)}\end{bmatrix} + {t\begin{bmatrix}1.00 \\{- 0.80} \\{- 1.05}\end{bmatrix}}} = {\begin{bmatrix}3.01 \\2.38 \\3.86\end{bmatrix} + {t\begin{bmatrix}1.00 \\{- 0.80} \\{- 1.05}\end{bmatrix}}}}}}$where t is an arbitrary scalar.

BR₀, CR₀ and ISF₀ may be used to represent the starting values forpersonalizing the therapeutic parameters. As described in the presentdisclosure, if only BR₀ is known, one or both of ISF and/or CR may bedetermined. For example, if only BR₀ is known,ln(CR₀)=4.79−0.80 ln(BR₀) andln(ISF₀)=7.03−1.05 ln(BR₀)

Personalizing one or more of the therapeutic parameters may includeincrementally moving daily settings of the therapeutic parameters closerto the “physiology line” of FIG. 8. For example, the therapeuticparameters may be personalized by taking the average x_(n+1) of a) apartial step toward the closest point X_(closest) on the physiologyline, x_(towards), and b) a partial step along the physiology line,X_(along).x _(closest) =x _(μ)+[(x _(n) −x _(μ))·m][m·m]⁻¹ mx _(towards) =x _(n)+speed_(towards)(x _(closest) −x _(n))where speed_(towards) represents how quickly the system shifts thetherapeutic parameters toward the physiology line. This value may takeany numerical value between 0 and 1. For example, a value of 0.5 may beused, or some other numeric value may be used to adjust how quickly thesystem shifts the therapeutic parameters toward the physiology line.Additionally, m represents the line parallel to the physiology linealong which x_(n) lies.x _(along) =x _(n)+speed_(along) mwhere speed_(along) represents how far the therapeutic parameters aremodified along a direction parallel to the physiology line.

${speed}_{along} = {0.1\frac{{mean}\left( {w_{i}{\ln\left( {{MBG}_{i}/{setpoint}} \right)}} \right)}{{mean}\left( w_{i} \right)}}$where setpoint may represent a target value of target blood glucoselevel. Thus, to find the new therapeutic parameter set personalizedbased on the previous data, the new set may be defined by:x _(n+1)=(x _(towards) −x _(along))/2Personalization Based on Bolus Doses

In some cases, the present disclosure may include personalization thatmay occur in the delivery of insulin that is further personalized basedon the delivery of bolus doses. For example, as described herein,personalization may occur at a first level by adjusting the delivery ofinsulin to be a multiple or ratio of a baseline basal rate for a user,such as delivering 0×, 1×, or 2× of the baseline basal rate.Personalization may also occur at a second level by analyzing whatmultiple or ratio of the baseline basal is delivered for a diurnal timeperiod and adjusting the baseline basal rate for that diurnal timeperiod or a related diurnal time period based on what adjustments weremade in the personalization at the first level. For example, if the userreceived 2× of the baseline basal rate for an entire diurnal timeperiod, the baseline basal rate may be increased for that diurnal timeperiod or a related diurnal time period in the future.

In some cases, a bolus that is delivered to a user may be treated as aseries of rate increases for a given time period. For example, if a userwas receiving 1× of the baseline basal rate for two hours and at the endof the two hours received a correction bolus, the correction bolus maybe treated as a series of times that 2× of the baseline basal rate wasdelivered to the user. In such a case, such a system may personalize atthe second level based on boluses, in addition to personalizations doneat the first level.

FIG. 10 illustrates an example graph 1000 illustrating a visualizationof back-filling based on a correction bolus. Along the x-axis of thegraph 1000 are time segments, and along the y-axis of the graph 1000 areratios of a baseline basal rate. FIG. 10 illustrates an example seriesof delivery actions 1010 based on a ratio of the baseline basal rate,including the delivery actions 1010 a-1010 g. FIG. 10 additionallyillustrates an example correction bolus 1020.

As illustrated in FIG. 10, during the first time segment, a user mayreceive 1× of the baseline basal rate as the delivery action 1010 a.Similarly, the user may receive 0× during the second time segment forthe delivery action 1010 b, 1× during the third time segment for thedelivery action 1010 c, 0× during the fourth time segment for thedelivery action 1010 d, 1× during the fifth time segment for thedelivery action 1010 e, 2× during the sixth time segment for thedelivery action 1010 f, and 2× during the seventh time segment for thedelivery action 1010 g. As illustrated, during the seventh time segment,the correction bolus 1020 may be delivered with an amount of insulincomparable to approximately 4× of the baseline basal rate in addition tothe baseline basal insulin delivered at 2× the baseline basal rate.

After receiving the correction bolus 1020, a control device may backfill one or more of the time segments in which the first level ofpersonalization had availability to deliver a higher amount of insulin.For example, with reference to FIG. 10, the delivery action 1010 eduring the fifth time segment delivered 1× the baseline basal rate, andthe control device may change the delivery amount to 2× such that aspersonalization at the second level is performed for a diurnal timeblock including the fifth time segment, the personalization at thesecond level will account for a 2× delivery during the fifth timesegment rather than a 1× delivery. Thus, the historical delivery ofbasal insulin used in personalization at the second level may beadjusted such that the correction bolus is accounted for in thehistorical delivery information.

In some embodiments, the control device may obtain the maximum baselinebasal rate available for personalization at the first level. Forexample, the personalization at the first level may allow delivery of0×, 1×, and 2×, or may allow any other ratio or multiple of the baselinebasal rate, such as 3×, 5×, or others. In these and other embodiments,the control device may determine what time segments had potentialinsulin delivery segments. The term insulin delivery segments may referto one delivery action of the baseline basal rate, or a 1× deliveryaction. For example, with reference to FIG. 10, the delivery action 1010a may fill one insulin delivery segment. In some embodiments, theinsulin delivery segments identified as potential may be available overa back-fill time, or may be limited to insulin delivery segments withinthe back-fill time. The back-fill time may be a static time value, suchas one hour, two hours, three hours, four hours, six hours, twelvehours, or others. Additionally or alternatively, the back-fill time maybe based on one or more attributes of the user, such as ISF, CR, orothers. For example, with reference to FIG. 10, the back-fill time maybe the previous six time segments.

With reference to FIG. 10, based on a maximum delivery of 2× and theback-fill time including the previous six time segments and the deliveryactions 1010, FIG. 10 illustrates one insulin delivery segment availablein the first time segment, two insulin delivery segments available inthe second time segment, one insulin delivery segment available in thethird time segment, two insulin delivery segments available in thefourth time segment, and one insulin delivery segment available in thefifth time segment. Thus, with reference to FIG. 10, there may be seveninsulin delivery segments potentially available to be back-filled duringthe back-fill time.

In some embodiments, the control device may determine the amount ofcumulative IOB if all of the insulin delivery segments were filled. Forexample, with reference to FIG. 10, the control device may determinewhat the cumulative IOB would be at the time of the correction bolus ifall seven insulin delivery segments were back filled such that 2× of thebaseline basal insulin rate had been delivered for each of the timesegments during the back-fill time. The IOB may be determined asdescribed herein.

In some embodiments, the control device may determine how many insulindelivery segments to back-fill based on one or more criteria. Forexample, the control device may determine how many insulin deliverysegments to back-fill based on determining what the cumulative IOB wouldbe right before delivery of the correction bolus based on back-fillingone or more insulin delivery segments. For example, with reference toFIG. 10, the control device may determine that the correction bolusincludes 1.2 Units of insulin. The control device may determine thatback-filling all of the insulin delivery segments may result in an IOBof 1.8 Units of insulin. The control device may then iterativelydetermine the IOB if fewer than all of the insulin delivery segmentswere back-filled, until the IOB is equal to or less than the correctionbolus. In some embodiments, such an iterative process may proceed oneinsulin delivery segment at a time, or may follow any other algorithmicapproach to identify the point at which the IOB is equal to or less thanthe correction bolus.

As another example, the control device may determine how many insulindelivery segments to back-fill based on the cumulative amount of insulinin the back-filled insulin delivery segments. For example, withreference to FIG. 10, the control device may determine that thecorrection bolus includes 1.2 Units of insulin. The control device maydetermine that back-filling all of the insulin delivery segments mayinclude 2.1 Units of insulin. The control device may then iterativelydetermine the cumulative amount of insulin if fewer than all of theinsulin delivery segments were back-filled, until the cumulative amountof insulin is less than the correction bolus of 1.2 Units of insulin. Insome embodiments, such an iterative process may proceed one insulindelivery segment at a time, or may follow any other algorithmic approachto identify the first insulin delivery segment at which the cumulativeinsulin is less than the correction bolus.

In some embodiments, the control device may utilize more than one basisfor determining the number of insulin delivery segments to back-fill.For example, for each iteration of back-filling an insulin deliverysegment, the control device may check whether the IOB is equal to orless than the correction bolus and may also check whether the cumulativeinsulin of the back-filled insulin delivery segments is less than thecorrection bolus.

In some embodiments, after a number of the insulin delivery segmentshave been back-filled, the control device may perform a personalizationat the second level (e.g., adjusting one or more of BR, CR, and ISF)based on the personalization that occurred at the first level and theback-filled insulin delivery time segments. For example, with referenceto FIG. 10, after the back-filling, the historical insulin deliveryinformation shows that 2× of the baseline basal insulin was deliveredduring the third, fourth, fifth, sixth, and seventh time segments. Thus,for a diurnal time period corresponding to those time segments, anypersonalization may occur, such as an increase of BR, decrease of CR,decrease of ISF, or any combinations thereof. In some embodiments,personalization at the second level may occur some time later than theback-filling. For example, the back-filling may occur concurrent with orwithin a short time (e.g., 5 minutes, 10 minutes, 30 minutes, 1 hour) ofthe correction bolus, and the personalization at the second level mayoccur a longer time later (e.g., 6 hours, 12 hours, 24 hours) after thecorrection bolus.

In some embodiments, a similar process of back-filling may occur for anegative correction bolus. For example, the control device may instructa user that their blood glucose level is trending low and recommend thatthe user eat a certain number of carbohydrates to counteract the lowblood glucose level. Additionally or alternatively, the control devicemay adjust a bolus during a meal or adjust the baseline basal insulindelivery to be below what is expected such that a negative correctionbolus may be provided. For example, if a user has a certain CR and ISFsuch that a bolus calculator would normally recommend 2 Units of insulinfor a meal they are about to eat, the control device may insteadrecommend a bolus of only 1.2 Units of insulin as a negative correctionbolus. Based on the number of carbohydrates that a user indicates theyhave consumed, the number of carbohydrates recommended to be consumed,or the amount of insulin below the expected delivery, the control devicemay back-fill the insulin delivery segments to a lower historicaldelivery amount. For example, with reference to FIG. 10, if the controldevice had lowered a meal bolus rather than a correction bolus, one ormore of the delivery actions 1010 g and 1010 f may be reduced from 2× to1× or 0×, and one or more of the delivery actions 1010 e, 1010 c, and1010 a from 1× to 0×.

In some embodiments, back-filling to reduce the historical insulindelivery actions for a negative correction bolus may be based on thecumulative IOB. For example, the control device may readjust one or moreof the insulin delivery actions and determine what a hypothetical IOBwould have been based on the adjusted delivery actions. The controldevice may iteratively adjust the number of insulin delivery segmentsthat are back-filled to reduce the insulin delivery actions until thereduction in IOB is equal to or less than the negative correction bolus,or the cumulative insulin reduction is less than the negative correctionbolus.

In some embodiments, the basal adjustments may be representedmathematically by an equation correlating a correction bolus to a seriesof delivery actions:Correction Bolus_(t)=Σ_(k=0) ^(n)α_(i) ^(k)action_(t-k)  Equation (6)where Correction Bolus represents a correction bolus at time t,BY_(t)=Y_(t-1), B²Y_(t)=Y_(t-2), B^(k)Y_(t)=Y_(t-k), α_(t)=e^(−ts/τ)^(i) , τ_(i) represents the insulin time constant (such as approximately120 minutes), and action_(t) [U] represents an amount of insulindelivered for a time segment at the basal rate at time t.

Modifications, additions, or omissions may be made to FIG. 10 withoutdeparting from the scope of the present disclosure. For example, anynumber of time segments may be included in the back-fill time, such asone hour, two hour, three hours, four hours, five hours, or six hours.As another example, any number of correction boluses may be accountedfor. As an additional example, any ratio of delivery may be included,such as 0×, 0.5×, 1×, 1.5×, 2×, 3×, 5×, or others.

FIG. 11 is a flowchart of an example technique for adjusting basalinsulin delivery rates, in accordance with one or more embodiments ofthe present disclosure. FIG. 11 depicts an example method 250 foroperation of a diabetes management system, such as system 10 depicted inFIG. 1. As shown in FIG. 11, a system can receive user inputs, such asuser inputs at blocks 251 and 252, which can be used to provide initialsettings, such as one or more target blood glucose values that may beused or determined at block 261 and/or one or more user-specific dosageparameters that may be used or determined at block 262. In some cases,user inputs at blocks 251 and 252 can be entered by a PWD, a caregiverfor the PWD, or a healthcare professional. In some cases, user inputs atblocks 251 and 252 can be entered on a mobile computing device 60, suchas a smartphone. Based on the user-specific dosage parameters, themethod 250 can generate multiple basal insulin delivery profiles and/orrates at block 263. In some cases, the plurality of basal insulindelivery profiles and/or rates can be based upon one or more baselinebasal rates. At block 264, the method 250 can analyze each basaldelivery profile or rate generated at block 263 based on variations ofpredicted future blood glucose values from one or more target bloodglucose values (such as the target blood glucose values from block 261)using blood glucose data from a continuous glucose monitor (CGM) orblood glucose meter (BGM), such as generated in block 271. In somecases, the blood glucose data can be from the continuous glucose monitor50 from the system 10 of FIG. 1. As will be discussed below, predictedblood glucose values for each generated basal delivery profile or ratecan use user-specific dosage parameters (for example, those determinedor otherwise adjusted at block 262). Additionally, predicted bloodglucose values can include inputs regarding previous dosages of insulinand/or food consumption (e.g., estimates of carbohydrates consumed). Insome cases, predicted blood glucose values used at block 264 canconsider data indicative of exercise, sickness, or any other physicalstate that might impact blood glucose levels in a PWD. Based on ananalysis of a variation of predicted blood glucose levels performed atblock 264, a basal delivery profile or rate generated at block 263 canbe selected at block 265, and the system can deliver basal insulinaccording to that selected basal delivery profile or rate to the PWD fora select period of time at block 272. In some cases, the pump assembly15 of system 10 of FIG. 1 can be used to deliver the insulin. In somecases, the blocks 263, 264, 265, and 272 can each be conducted byreusable pump controller 200 of system 10. In some cases, the blocks271, 263, 264, and 265 can all be conducted by continuous glucosemonitor 50 of system 10, with data regarding the selected delivery ratebeing sent to reusable pump controller 200. In some cases, the blocks251, 252, 261, 262, 263, 264, and 265 can all be conducted on mobilecomputing device 60 of system 10 of FIG. 1, with data regarding theselected delivery rate being sent to reusable pump controller 200 fromthe mobile computing device 60.

Methods and systems provided herein can additionally update or adjustuser-specific dosage parameters at block 262 and can update or adjustthe blood glucose targets at block 261 based on the selected basaldelivery profiles and/or rates selected at block 265 or based on bloodglucose data obtained at block 271. In some cases, at block 281, periodsof time when a selected basal delivery was different from baseline basalrate for that period of time can be detected. For these select periodsof time (e.g., diurnal time segments), at block 262 the user-specificdosage parameters can be adjusted for that period of time. For example,if the selected basal delivery for a time block exceeds the baselinebasal rate for that time block, at block 262 the system 10 can increasethe baseline basal rate for that time block (e.g., a diurnal period) orsome other related time block (such as the preceding diurnal period).For example, if the selected basal delivery from 2 PM to 3 PM exceededthe baseline basal rate for that time, the system 10 may increase thebaseline basal rate for 2 PM to 3 PM or may adjust the baseline basalrate for 1 PM to 2 PM, 12 PM to 1 PM and/or 11 AM to 12 PM. In somecases, each adjustment to a baseline basal rate is less than thedifference between the baseline basal rate and the selected basaldelivery. In some cases, each adjustment can be a predetermined amount(e.g., baseline basal rate adjusted up or down by 0.5 Unit/hour, 0.3Unit/hour, 0.1 Unit/hour) or percentage (e.g., 5%, 3%, 1%), which canlimit the change to the user-specific dosage parameters due to anirregular event. At block 283, the variability of blood glucose data canbe analyzed to make adjustments to the blood glucose target(s) at block261. For example, at block 283, a blood glucose data distribution can bedetermined for a diurnal period (e.g., between 1 AM and 2 AM) todetermine a measure of dispersion of blood glucose values for the PWDduring that diurnal period, and at block 261 adjustments can be made tothe blood glucose target for that diurnal period, and/or adjacentperiods, based on the measure of dispersion.

Each of the processes discussed in regards to FIG. 11 are discussed atfurther length below.

In some cases, a diabetes management system may have a threshold beyondwhich it ceases personalizing BR, CSF, and/or CR for various diurnaltime blocks. For example, if the system monitors variations in thesetherapeutic parameters and determines that no modification has beenperformed in a certain amount of time (e.g., one week, two weeks, amonth, etc.), the system may stop monitoring for and performing dailyupdates, and may revert to a periodic check to verify that thetherapeutic parameters are still at the optimal parameters for the PWD.In these and other cases, as the system shifts away from modifying thetherapeutic parameters for the various diurnal time blocks, the systemmay provide a notification of how close the therapeutic parameters arefor the PWD as compared to the general probability distribution for thetherapeutic parameters. For example, in a manner similar to thatdescribed above with respect to determining the Mahalanobis Distancewith respect to initial values, the system may perform a similaranalysis and inform a user (such as the PWD or a caregiver of the PWD)of the score of the optimal therapeutic parameters for the PWD. In somecases, these optimal values and/or the associated score may be providedto a manufacturer of diabetes management systems to update and/or modifythe general probability distributions.

FIG. 12 illustrates a flowchart of an example method 1200 of adjusting abasal insulin rate. The method 1200 may be performed by any suitablesystem, apparatus, or device. For example, the system 10, the pumpassembly 15, the mobile computing device 60 of FIG. 1, and/or a remoteserver may perform one or more of the operations associated with themethod 1200. Although illustrated with discrete blocks, the steps andoperations associated with one or more of the blocks of the method 1200may be divided into additional blocks, combined into fewer blocks, oreliminated, depending on the desired implementation.

At block 1210, a recommended bolus dose of insulin for a PWD may beprovided. For example, a user interface of a mobile electronic device oran insulin pump may display a recommended bolus dose based on a highblood glucose level or an input regarding an upcoming meal.

At block 1220, a user override of the recommended bolus dose may bereceived such that a different amount of insulin is delivered. Forexample, a user of an input device may adjust the recommended bolus doseto a larger dose such that more insulin than recommended is delivered tothe PWD.

At block 1230, for a time period after the user override, the automatedmodification of a baseline basal rate may be locked out. For example,the lockout may prevent a diabetes management system from delivering aratio of the baseline basal rate during one or more diurnal timeperiods. As another example, the lockout may prevent the system fromconsidering blood glucose levels during one or more of the diurnal timeblocks affected by the manually modified bolus dose in adjusting thebaseline basal rate for future diurnal time blocks.

Modifications, additions, or omissions may be made to the method 1200without departing from the scope of the present disclosure. For example,the operations of the method 1200 may be implemented in differing order.Additionally or alternatively, two or more operations may be performedat the same time. Furthermore, the outlined operations and actions areprovided as examples, and some of the operations and actions may beoptional, combined into fewer operations and actions, or expanded intoadditional operations and actions without detracting from the essence ofthe disclosed embodiments.

FIG. 13 illustrates a flowchart of an example method 1300 of adjusting abasal insulin rate. The method 1300 may be performed by any suitablesystem, apparatus, or device. For example, the system 10, the pumpassembly 15, the mobile computing device 60 of FIG. 1, and/or a remoteserver may perform one or more of the operations associated with themethod 1300. Although illustrated with discrete blocks, the steps andoperations associated with one or more of the blocks of the method 1300may be divided into additional blocks, combined into fewer blocks, oreliminated, depending on the desired implementation.

At block 1305, blood glucose data may be received regarding a PWD. Forexample, a CGM or a BGM may measure a blood glucose level and mayprovide that reading to a control device.

At block 1310, basal insulin may be delivered to the PWD from an insulinpump based on the blood glucose data to achieve a desired blood glucoselevel. For example, as described herein, the insulin pump may receive amessage to deliver a ratio of a baseline basal rate of insulin based onthe blood glucose data.

At block 1315, the stored basal insulin deliver rates, ISF, and/or CRfor future time periods may be altered based on the amount of insulindelivered during past time periods. For example, as described herein, ifa diabetes management system delivered 2× the baseline basal rate for adiurnal time period, the system may increase the baseline basal rate forthat diurnal time period.

At block 1320, a bolus inquiry may be received from a user. For example,a PWD may interact with an electronic device to indicate that the PWDfeels as though their blood sugar is high. As another example, a PWD mayindicate that they are preparing to eat a meal and input the number ofgrams of carbohydrates that the PWD intends to eat at the meal.

At block 1325, a recommended bolus dose to achieve the desired bloodglucose level may be provided. The recommended bolus dose may be basedon consumption of carbohydrates, an elevated blood glucose level, orboth. In some embodiments, the recommended bolus dose may be presentedto a user of an electronic device.

At block 1330, a user override of the recommended bolus dose may bereceived from a user. For example, a user may manually adjust the bolusdose to be larger or smaller.

At block 1335, a determination may be made as to whether there has beena recent user override. If there have been no recent user overrides, themethod 1300 may return to block 1305 to start over. If there has been arecent user override, the method 1300 may proceed to block 1340.

At block 1340, an amount of insulin delivered during past time periodsmay be ignored for a lockout time period. For example, in determiningwhether to deliver a ratio of a baseline basal rate to a PWD, a systemmay normally consider basal and/or bolus insulin delivered indetermining what ratio of a baseline basal rate to deliver. However,after a lockout, such information may be ignored or given less weight.The method 1300 may then proceed to block 1315.

After returning to block 1315, the alteration of one or more of thetherapeutic parameters may ignore the amount of insulin delivered perthe block 1340. For example, the alteration may be based only on diurnaltime blocks unaffected by the overridden bolus dose, and/or may defaultto only delivering the baseline basal rate rather than a ratio of thebaseline base rate.

Modifications, additions, or omissions may be made to the method 1300without departing from the scope of the present disclosure. For example,the operations of the method 1300 may be implemented in differing order.Additionally or alternatively, two or more operations may be performedat the same time. Furthermore, the outlined operations and actions areprovided as examples, and some of the operations and actions may beoptional, combined into fewer operations and actions, or expanded intoadditional operations and actions without detracting from the essence ofthe disclosed embodiments.

FIG. 14 illustrates a flowchart of an example method 1400 of providing arecommendation of one or more therapeutic parameters. The method 1400may be performed by any suitable system, apparatus, or device. Forexample, the system 10, the pump assembly 15, the mobile computingdevice 60 of FIG. 1, and/or a remote server may perform one or more ofthe operations associated with the method 1400. Although illustratedwith discrete blocks, the steps and operations associated with one ormore of the blocks of the method 1400 may be divided into additionalblocks, combined into fewer blocks, or eliminated, depending on thedesired implementation.

At block 1410, a daily BR, CR, and/or ISF may be received. For example,a user may manually input a BR, CR, and/or ISF for a PWD when beginninginsulin treatment, or when setting up a new device or software featureassociated with treating diabetes of the PWD. As another example, a usermay manually modify an existing BR, CR, and/or ISF. In some cases, onlyone or two of the therapeutic parameters may be input and one or moreother therapeutic parameters may be derived from mathematicalrelationships between the therapeutic parameters.

At block 1420, a first relationship between the BR, CR, and/or ISF maybe determined. For example, the values may be input into a matrix, ormay be grouped together as an initial set of therapeutic values. Asanother example, for a received BR, the corresponding CR and ISF may beplotted in a chart.

At block 1430, the first relationship may be compared with a probabilitydistribution of a second relationship between the general BR, generalCR, and general ISF. For example, in some embodiments, a plot of a sliceof the probability distribution of the general CR and general ISF forthe BR received at block 1410 may be provided and the first relationshipmay be plotted on the same figure. Additionally or alternatively, thefirst relationship may be numerically analyzed relative to the generalprobability distribution such as using a Mahalanobis Distance.

At block 1440, a determination may be made as to whether the distancebetween the first relationship and the probability distribution exceedsa threshold. If it is determined that the distance does not exceed thethreshold, the method 1400 may finish. If it is determined that thedistance does exceed a threshold, the method 1400 may proceed to block1450.

At block 1450, a user interface may be generated indicating that thedistance exceeds the threshold and providing a recommended variation inone or more of the therapeutic parameters. For example, a user interface(such as that illustrated in FIG. 6) may illustrate a chart of thelocation of the first relationship and a recommendation of variation toone or both of CR and ISF. For example, the recommended variation maymove the first relationship closer to the probability distribution.

Modifications, additions, or omissions may be made to the method 1400without departing from the scope of the present disclosure. For example,the operations of the method 1400 may be implemented in differing order.Additionally or alternatively, two or more operations may be performedat the same time. Furthermore, the outlined operations and actions areprovided as examples, and some of the operations and actions may beoptional, combined into fewer operations and actions, or expanded intoadditional operations and actions without detracting from the essence ofthe disclosed embodiments.

FIG. 15 illustrates a flowchart of an example method 1500 of deliveringinsulin. The method 1500 may be performed by any suitable system,apparatus, or device. For example, the system 10, the pump assembly 15,the mobile computing device 60 of FIG. 1, and/or a remote server mayperform one or more of the operations associated with the method 1500.Although illustrated with discrete blocks, the steps and operationsassociated with one or more of the blocks of the method 1500 may bedivided into additional blocks, combined into fewer blocks, oreliminated, depending on the desired implementation.

At block 1510, a daily BR may be received. For example, a user may inputa BR for a PWD into an electronic device.

At block 1520, a CR may be determined based on the BR. For example, adiabetes management system may utilize a mathematical relationship todetermine the CR from the BR. Such a relationship may include thatexpressed in Equation 2.

At block 1530, a bolus dose may be calculated based on the CR. Forexample, if a PWD is going to eat a meal and inputs an amount ofcarbohydrates to be consumed during the meal, the diabetes managementsystem may use the CR determined at block 1520 to calculate the amountof bolus dose required.

At block 1540, a message may be generated for an insulin delivery deviceto deliver the bolus dose calculated at block 1530. Such a deliverydevice may include an insulin pump, or an injection based deliverymechanism such as a pen or syringe.

At block 1550, the bolus dose may be delivered by the insulin deliverydevice.

Modifications, additions, or omissions may be made to the method 1500without departing from the scope of the present disclosure. For example,the operations of the method 1500 may be implemented in differing order.Additionally or alternatively, two or more operations may be performedat the same time. Furthermore, the outlined operations and actions areprovided as examples, and some of the operations and actions may beoptional, combined into fewer operations and actions, or expanded intoadditional operations and actions without detracting from the essence ofthe disclosed embodiments.

FIG. 16 illustrates a flowchart of an example method 1600 of deliveringinsulin. The method 1600 may be performed by any suitable system,apparatus, or device. For example, the system 10, the pump assembly 15,the mobile computing device 60 of FIG. 1, and/or a remote server mayperform one or more of the operations associated with the method 1600.Although illustrated with discrete blocks, the steps and operationsassociated with one or more of the blocks of the method 1600 may bedivided into additional blocks, combined into fewer blocks, oreliminated, depending on the desired implementation.

At block 1610, a daily BR may be received. For example, a user may inputa BR for a PWD into an electronic device.

At block 1620, an ISF may be determined based on the BR. For example, adiabetes management system may utilize a mathematical relationship todetermine the ISF from the BR. Such a relationship may include thatexpressed in Equation 1.

At block 1630, a bolus dose may be calculated based on the ISF. Forexample, if a PWD has a high blood glucose level, the diabetesmanagement system may use the ISF determined at block 1620 to calculatethe amount of bolus dose required.

At block 1640, a message may be generated for an insulin delivery deviceto deliver the bolus dose calculated at block 1630. Such a deliverydevice may include an insulin pump, or an injection based deliverymechanism such as a pen or syringe.

At block 1650, the bolus dose may be delivered by the insulin deliverydevice.

Modifications, additions, or omissions may be made to the method 1600without departing from the scope of the present disclosure. For example,the operations of the method 1600 may be implemented in differing order.Additionally or alternatively, two or more operations may be performedat the same time. Furthermore, the outlined operations and actions areprovided as examples, and some of the operations and actions may beoptional, combined into fewer operations and actions, or expanded intoadditional operations and actions without detracting from the essence ofthe disclosed embodiments.

FIG. 17 illustrates a flowchart of an example method 1700 of adjusting abasal insulin rate. The method 1700 may be performed by any suitablesystem, apparatus, or device. For example, the system 10, the pumpassembly 15, the mobile computing device 60 of FIG. 1, and/or a remoteserver may perform one or more of the operations associated with themethod 1700. Although illustrated with discrete blocks, the steps andoperations associated with one or more of the blocks of the method 1700may be divided into additional blocks, combined into fewer blocks, oreliminated, depending on the desired implementation.

At block 1710, a shelf-life risk score may be determined. For example, adiabetes management system may track the age of an insulin cartridgeassociated with a pump and assign a certain shelf-life risk score basedon the age of the insulin cartridge. As another example, the system maymonitor the number of times the system has delivered an amount ofinsulin above the baseline basal rate. For example, if the systemrepeatedly delivers a ratio of 2× the baseline basal rate, the systemmay provide a shelf-life risk score based on how frequently the largerratio is delivered. In some cases, the shelf-life risk score may bebased on both age and number of times the larger dose is delivered.

At block 1720, a determination is made as to whether the shelf-life riskscore exceeds a threshold. If it does not exceed a threshold, the methodmay end and the diabetes management system may proceed with normaloperation. If it does exceed the threshold, the method 1700 may proceedto block 1730.

At block 1730, the automated modification of a baseline basal insulinrate may be locked out until the insulin is replaced. For example,similar to block 1230 of FIG. 12, the system may be prevented frommodifying the baseline basal insulin rate for a certain period of time.In the method 1700, the period of time may be based on when the insulinis replaced. For example, if the threshold is crossed at a first diurnaltime block and the insulin is replaced at a second diurnal time blocklater in time, the span of time between the first diurnal time block andthe second diurnal time block may prevent lockout automated modificationof the basal rate. Such lockout may prevent the system from delivering aratio of the baseline basal rate and may default to delivering thebaseline basal rate. Additionally or alternatively, such lockout mayprevent the system from adjusting the baseline basal rate for thediurnal time block or a related diurnal time block in the future basedon the amount of insulin delivered or the effectiveness of the deliveredinsulin during the time period when the shelf-life risk score thresholdwas exceeded.

At block 1740, a message may be generated that the shelf-life risk scorehas exceeded the threshold. For example, a message may be generated thatthe system may stop providing personalization until the insulin isreplaced. As another example, the message may instruct a user to replacean insulin cartridge immediately with instructions of how to do so. Asanother example, the message may lockout certain interactive features ofthe system until the insulin has been replaced.

Modifications, additions, or omissions may be made to the method 1700without departing from the scope of the present disclosure. For example,the operations of the method 1700 may be implemented in differing order.Additionally or alternatively, two or more operations may be performedat the same time. Furthermore, the outlined operations and actions areprovided as examples, and some of the operations and actions may beoptional, combined into fewer operations and actions, or expanded intoadditional operations and actions without detracting from the essence ofthe disclosed embodiments.

FIG. 18 illustrates a flowchart of an example method 1800 ofpersonalizing an insulin delivery rate based on a correction bolus. Themethod 1800 may be performed by any suitable system, apparatus, ordevice. For example, the system 10, the pump assembly 15, the mobilecomputing device 60 of FIG. 1, and/or a remote server may perform one ormore of the operations associated with the method 1800. For example, themobile computing device 60 may operate as a control device. Althoughillustrated with discrete blocks, the steps and operations associatedwith one or more of the blocks of the method 1800 may be divided intoadditional blocks, combined into fewer blocks, or eliminated, dependingon the desired implementation.

FIG. 18 may operate within the context of two layers of personalization.

Personalization at a first level may occur as a user may be providedwith insulin at a ratio or multiple of a baseline basal rate asdescribed in herein. For example, based on a delivery profile, the usermay be provided with 0×, 1×, or 2× of the baseline basal rate for eachdelivery action. Personalization at a second level may occur as acontrol device analyzes the delivery actions for a diurnal time periodand adjusts one or more of the BR, CR, and ISF for a related diurnaltime period based on the personalization that occurred at the firstlevel, as described herein.

At block 1810, a determination may be made as to a number of availableinsulin delivery segments for a back-fill time. For example, a controldevice (such as the mobile computing device 60 of FIG. 1) may obtain anindication of a correction bolus, and may identify one or more deliveryactions within the back-fill time relative to the correction bolus. Forexample, if the back-fill time is two hours, the control device mayidentify delivery actions within the two hours prior to the correctionbolus where less than a maximum amount of allowed insulin was deliveredbased on personalization at the first level. For example, if twodelivery actions delivered 1× insulin of a possible 2× delivery, bothdelivery actions would provide an available insulin delivery segment toincrease to 2× delivery of insulin. In some embodiments, the block 1810may be applied to available insulin delivery segments that may beback-filled to reduce the insulin delivered. For example, if twodelivery actions delivered 1× insulin, both delivery actions wouldprovide an available insulin delivery segment to be reduced to 0×insulin.

At block 1820, a determination may be made as to what the cumulative IOBwould be if all available insulin delivery segments were back-filled.For example, with respect to increasing insulin delivery (such as whenpersonalizing for a correction bolus), the control device may calculatethe IOB if each delivery action delivered the maximum allowed ratio ormultiple of the baseline basal insulin rate, such as 2×. As anadditional example, with respect to decreasing insulin delivery (such aswhen personalizing for a negative correction bolus), the control devicemay calculate the reduction in IOB if each delivery action delivered theminimum allowed ratio or multiple of the baseline basal insulin rate,such as 0×.

At block 1830, one or more insulin delivery segments may be back-filledto adjust the historical delivery data. For example, the control devicemay back-fill all available segments, either to account for a correctionbolus or a negative correction bolus. Additionally or alternatively, themethod 1800 may iteratively return to the block 1830 such that thenumber of back-filled insulin delivery segments may be decreased orincreased. In some embodiments, the number of insulin delivery segmentsback-filled may be adjusted one segment at a time, or may follow someother algorithm such as an optimization algorithm, a minimum-findingalgorithm, a maximum-finding algorithm, or others.

At block 1840, a determination may be made as to whether the cumulativeIOB is less than or equal to a correction bolus. For example, thecumulative IOB based on the number of insulin delivery segmentsback-filled at the block 1830 may be compared to a correction bolusdelivered to a user, or requested to be delivered to a user. If thecumulative IOB is less than or equal to the correction bolus, the method1800 may proceed to block 1860. If the cumulative IOB is greater thanthe correction bolus, the method 1800 may proceed to block 1850. In someembodiments, the inquiry may determine if the IOB reduction is less thanor equal to a negative bolus.

At block 1850, a determination may be made as to whether the cumulativeinsulin of the back-filled insulin delivery segments is less than thecorrection bolus. For example, the control device may determine thecumulative amount of insulin from the number of back-filled insulindelivery segments back-filled at the block 1830. If the cumulativeinsulin that was back-filled is less than the correction bolus, themethod 1800 may proceed to the block 1860. If the cumulative insulinthat was back-filled is not less than the correction bolus, the method1800 may return to the block 1830 to adjust the number of insulindelivery segments that are back-filled. In some embodiments, the inquirymay determine if the reduction in insulin is less than the negativebolus.

At block 1860, the back-filling process may be stopped. For example, thecontrol device may cease adjusting the number of back-filled insulindelivery segments and may make the delivery actions adjusted by theback-filled insulin delivery segments the historical informationregarding insulin delivery for the respective time segments.

At block 1870, personalization may be performed at the second level. Forexample, the control device may utilize the historical informationregarding insulin delivery as adjusted using the back-filled insulindelivery segments to adjust one or more of the BR, CR, and ISF of a userfor a diurnal time block related to the diurnal time block that includesthe time segments with back-filled insulin delivery segments.

Modifications, additions, or omissions may be made to the method 1800without departing from the scope of the present disclosure. For example,the operations of the method 1800 may be implemented in differing order.Additionally or alternatively, two or more operations may be performedat the same time. Furthermore, the outlined operations and actions areprovided as examples, and some of the operations and actions may beoptional, combined into fewer operations and actions, or expanded intoadditional operations and actions without detracting from the essence ofthe disclosed embodiments.

In some embodiments, the method 1800 may be modified as described toaccount for a negative bolus rather than a correction bolus.Additionally, the method 1800 has been described with a perspective ofback-filling all available segments and then decreasing the number ofback-filled segments until the cumulative IOB or the cumulative insulinis less than the correction bolus. In some embodiments, the perspectivemay be shifted such that additional insulin delivery segments may beback-filled until the cumulative IOB or the cumulative insulin isgreater than the correction bolus.

In some embodiments, the method 1800 may be modified such that one ormore of the blocks 1840-1860 are omitted and the number of insulindelivery segments of the block 1830 may be limited by the back-filltime. Additionally or alternatively, the method 1800 may be modifiedsuch that no back-fill time may be used as a constraint and theconstraints on the number of insulin delivery segments that may beback-filled are based on the cumulative IOB and/or the cumulativeinsulin relative to the correction bolus.

The following description relates, generally, to medicine deliverysystems and methods. Such systems and methods may be used and performed,respectively, by a user, for example, a person with diabetes (PWD). ThePWD may live with type 1, type 2, or gestational diabetes. In somecases, a user can be a healthcare professional or caregiver for a PWD.In some embodiments, the systems and methods may be used to personalizeinsulin delivery to a PWD.

The drawings in FIGS. 19 through 24 may use reference labels thatinclude the same reference numbers (e.g., “400,” “900,” etc.) or asimilar range of reference numbers as other drawings. For clarity, an“X-” may be used herein to denote that a reference label relates toFIGS. 19 through 24, and not another figure that uses a similarreference number, for example, “X-400” or “X-970.”

Methods and systems provided herein can use information from a glucosemeasurement device (e.g., a continuous glucose monitor) to haveup-to-date blood glucose data (e.g., a plurality of blood glucose datapoints each hour) for the PWD in order to determine how to adjust basalinsulin delivery rates. In some cases, methods and systems providedherein can use blood glucose data from both one or more continuousglucose monitors and one or more blood glucose meters. Methods andsystems provided herein can be part of a hybrid closed-loop system (forexample, where basal rates can be adjusted automatically and the PWD canmanually enter or deliver a bolus). In some cases, methods and systemprovided herein can be part of a fully closed-loop system (for example,where basal rates can be adjusted automatically and boluses can bedelivered automatically). In some cases, “up-to-date” may mean less than1 hour old, less than 30 minutes old, or less than 15 minutes old.

Methods and systems provided herein can use a model to predict multiplefuture blood glucose levels for multiple different basal insulindelivery profiles or basal insulin delivery rates, and select one of thebasal insulin delivery profiles or basal insulin delivery rates based onprediction of which profile or rate will approximate a target bloodglucose level, or more specifically, select the profile that minimizesthe differences between the predicted future blood glucose values andone or more target blood glucose values. In some cases, the profile thatminimizes, lessons, or lowers variations from one or more target bloodglucose levels in the future may be selected. The selected basal profilecan then be delivered to the PWD at least until a process of evaluatingdifferent basal insulin delivery profiles or rates is repeated. In somecases, methods and systems provided herein can repeat a process ofevaluating multiple different basal insulin delivery profiles or basalinsulin delivery rates at a time interval that is less than the timeinterval for the plurality of future predicted blood glucose values. Forexample, in some cases, the time interval between evaluating andselecting from multiple different basal insulin delivery profiles orbasal insulin delivery rates can be less than one hour while theplurality of future predicted blood glucose values can extend over atime interval of at least two hours into the future. In some cases ofmethods and systems provided herein, each of the evaluated basal insulindelivery profiles or rates can extend for a time interval greater thanthe time interval between evaluation processes. In some cases, methodsand systems provided herein can evaluate insulin delivery profiles andrates that extend at least two hours into the future and predicted bloodglucose values can also be predicted over a time interval that extendsat least two hours into the future. In some cases, the profiles/ratesand time interval of predicted future blood glucose values extends atleast three hours into the future. In some cases, the profiles/rates andtime interval of predicted future blood glucose values extends a periodof time (e.g., at least four hours) into the future. In some cases, theprofiles/rates and time interval of predicted future blood glucosevalues extends at least five hours into the future. As used herein, theterm blood glucose level may include any measurement that estimates orcorrelates with blood glucose level, such as a detection of glucoselevels in interstitial fluids, urine, or other bodily fluids or tissues.

The different basal insulin delivery profiles or rates for eachevaluation process can be generated using any suitable techniques. Insome cases, multiple profiles or delivery rates are generated using oneor more user-specific dosage parameters. In some cases, one or moreuser-specific dosage parameters can be entered by a user, calculated byinformation entered by a user, and/or calculated by monitoring datagenerated from the PWD (e.g., monitoring insulin delivery rates andblood glucose data while the PWD is using a pump in an open loop mode).In some cases, methods and systems provided herein can modifyuser-specific dosage parameters over time based on one or more selectedbasal insulin delivery profiles or rates and/or other data obtained fromthe PWD. In some cases, the user-specific dosage parameters can bedosage parameters that are commonly used in the treatment of diabetes,such as average total daily insulin, total daily basal (TDB) insulin,average basal rate, insulin sensitivity factor (ISF), andcarbohydrate-to-insulin ratio (CR). For example, in some cases, a PWD'saverage basal rate can be used to calculate multiple different basalinsulin delivery profiles based on multiples or fractions of the averagebasal rate used over different intervals of time. In some cases, methodsand systems provided herein can use time-interval-specific user-specificdosage parameters (e.g., a time-interval-specific baseline basal rate).In some cases, methods and systems provided herein can make adjustmentsto time-interval-specific user-specific dosage parameters for each timeinterval for where a delivered basal rate varies from a baseline basalrate for that time interval. In some cases, user-specific dosageparameters are specific for time intervals of two hours or less, onehour or less, thirty minutes or less, or fifteen minutes or less. Forexample, in some cases methods and systems provided herein can store abaseline basal rate for the hour between 1 PM and 2 PM, and can adjustthe baseline basal rate for that hour up if the method or systemdelivers more basal insulin during that time period and adjust thebaseline basal rate down if the method or system delivers less basalinsulin during that time period. In some cases, adjustments touser-specific dosage parameters can be based on a threshold variationand/or can be limited to prevent excessive adjustments to user-specificdosage parameters. For example, in some cases, a daily adjustment to auser-specific dosage parameter can be limited to less than 10%, lessthan 5%, less than 3%, less than 2%, or to about 1%. In some cases, anadjustment to a baseline basal rate is less than a difference betweenthe amount of basal insulin actually delivered and the baseline basalfor a specific period of time (e.g., if a baseline basal rate is 1Unit/hour and systems or methods provided herein actually delivered 2Unit for the previous hour, the adjustment to any baseline basal ratebased on that difference would be less than 1 Unit/hour).

Methods and systems provided herein can use any appropriate model topredict multiple future blood glucose values. In some cases, predictivemodels can use one or more current or recent blood glucose measurements(e.g., from blood glucose meter and/or a continuous glucose monitor),estimates of rates of change of blood glucose levels, an estimation ofunacted carbohydrates, and/or an estimation of unacted insulin. In somecases, predictive models can use one or more user-specific dosageparameters in predicting multiple blood glucose values over a futuretime interval for multiple different basal insulin delivery profiles orrates over that same future time interval. As discussed above, thatfuture time interval can be at least two hours, at least three hours, orat least four hours, at least five hours, etc. User-specific dosageparameters, which can be time-interval-specific, can also be used indetermining an estimation of unacted carbohydrates and/or an estimationof unacted insulin. In some cases, an estimation of unactedcarbohydrates and/or an estimation of unacted insulin can use a simpledecay function. In some cases, an estimate of unacted insulin can bedetermined using an Insulin On Board (IOB) calculation, which are commonin the art of treating diabetes. In some cases, an IOB calculation usedin a predictive model used in methods and systems provided herein canconsider insulin delivered to the PWD during the delivery of a bolus. Insome cases, the IOB calculation can additionally add or subtract to theIOB based on changes to the basal insulin delivery rate from a baselinebasal rate. In some cases, an estimate of unacted carbohydrates can bedetermined using a Carbohydrates On Board (COB) calculation, which canbe based on a decay function and announced meals. In some cases,predictive models used in methods and systems provided herein can alsoconsider the non-carbohydrate components of a meal. In some cases,methods and systems provided herein can infer an amount of carbohydratesfrom an unannounced meal due to a spike in up-to-date blood glucosedata. In some cases, predictive models used in methods and systemsprovided herein can additionally consider additional health data orinputs, which may indicate that the PWD is sick, exercising,experiencing menses, or some other condition that may alter the PWD'sreaction to insulin and/or carbohydrates. In some cases, at least anIOB, a COB, an insulin sensitivity factor (ISF), and acarbohydrate-to-insulin ratio (CR) are used to predict future bloodglucose values for each evaluated basal insulin delivery profile orrate.

Methods and systems provided herein can set one or more blood glucosetargets using any suitable technique. In some cases, a blood glucosetarget can be fixed, either by a user or preprogrammed into the system.In some cases, the target blood glucose level can be time intervalspecific (e.g., based on diurnal time segments). In some cases, a usercan temporarily or permanently adjust the target blood glucose level. Insome cases, methods and systems provided herein can analyze thevariability of blood glucose data for specific days of the week and/orbased on other physiological patterns and adjust the blood glucosetargets for that individual based on the specific day of the week orbased on other physiological patterns. For example, a PWD may havecertain days of the week when they exercise and/or PWD may havedifferent insulin needs based on a menses cycle.

Methods and systems provided herein can evaluate each basal insulindelivery profile or rate to select the profile or rate that minimizes avariation from the one or more blood glucose targets using anyappropriate method. In some cases, methods and systems provided hereincan use a cost function to evaluate differences between the predictedblood glucose values for each basal insulin delivery profile or rate andblood glucose targets, potentially specified for a diurnal time segment.Methods and systems provided herein can then select a basal profile orrate that produces the lowest cost function value. Methods and systemsprovided herein can use any suitable cost function. In some cases, costfunctions can sum the absolute value of the difference between eachpredicted blood glucose value and each blood glucose target. In somecases, cost functions used in methods and systems provided herein canuse square of the difference. In some cases, cost functions used inmethods and systems provided herein can assign a higher cost to bloodglucose values below the blood glucose target in order reduce the riskof a hypoglycemic event. In some cases, the cost function can include asummation of the absolute values of a plurality of predicted deviations,squared deviations, log squared deviations, or a combination thereof. Insome cases, a cost function can include variables unrelated to thepredicted blood glucose values. For example, a cost function can includea penalty for profiles that do not deliver 100% of the BBR, thus addinga slight preference to use 100% of BBR. In some cases, methods andsystems provided herein can include a cost function that provides aslight preference to keep the existing basal modification for everyother interval (e.g., a second 15 minute segment), which could reducethe variability in basal insulin delivery rates in typical situations,but allow for more critical adjustments.

Methods and systems provided herein can receive various inputs from auser related to the delivery of basal insulin. In some cases, a user mayinput a fear of hypoglycemia (FHI) index. The FHI may indicate thepreference for or reticence to experience certain blood glucose levelsby the PWD. For example, the FHI may indicate that the PWD prefers“high” blood glucose levels (e.g., blood glucose levels above athreshold); or as another example, the FHI may indicate that the PWD isconcerned about “going low” (e.g., blood glucose levels below athreshold). In some cases, the FHI may correspond to a threshold and anacceptable probability of crossing the threshold, including using thethreshold to signify going high or using the threshold to signify goinglow, or both. In some cases, a probability of the PWD crossing thethreshold may be determined and a baseline basal insulin rate may bemodified to more closely align the acceptable probability of crossingthe threshold with the actual probability of crossing the threshold.Additionally or alternatively, the FHI may be used in other ways inmethods and systems of the present disclosure. For example, modificationof the baseline basal insulin rate for a diurnal period may be modifiedone way for a high FHI and another way for a low FHI. As anotherexample, multiple profiles of insulin delivery steps may use one set ofpossible steps for a high FHI, and another set of possible steps for alow FHI.

Methods and systems provided herein can modify or alter an insulindelivery profile or rate in any number of ways. In some cases, a usermay select a temporary override to indicate a user preference for aparticular blood glucose level. For example, the PWD may indicate thatthey are going for a long drive and do not want to have their bloodglucose levels drop below a certain level, and so may designate a targetblood glucose level higher than their normal target blood glucose level,which may be set for a particular or indefinite length of time. In somecases, methods and systems provided herein may modify or otherwiseselect a new profile or rate from multiple profiles that corresponds tothe blood glucose level from the temporary override. In some cases,methods and systems provided herein can permit a user to merely indicatea reduced tolerance for the risk of going low and can determine atemporary blood glucose level based on the variability of blood glucosedata for that PWD for previous days (optionally for a particular diurnaltime segment).

Methods and systems provided herein can store a plurality ofuser-specific dosage parameters (e.g., BBR, CR, and ISF) as differentvalues for a plurality of different diurnal time segments. As usedherein, “diurnal time segments” periods of time during each day, suchthat the methods and systems will repeat use of each diurnal-specificuser-specific dosage parameter during the same time on subsequent daysif a stored diurnal-specific user-specific dosage parameter is notmodified or change, thus the use of the stored diurnal-specificuser-specific dosage parameter will wrap each day. Methods and systemsprovided herein, however, can be adapted to make daily (or more or lessfrequent) adjustments to each diurnal-specific user-specific dosageparameter based on the operation of the system. Methods and systemsprovided herein may additionally store settings or adjustments forspecific days of the week or for other repeating cycles.

After a basal insulin delivery profile or rate is selected, methods andsystems provided herein can include the delivery of basal insulin to thePWD according to the selected basal insulin profile or rate for anysuitable period of time. In some cases, methods and systems providedherein may supply basal insulin according to the selected basal insulindelivery profile or rate for a predetermined amount of time that may beless than the time interval of the evaluated basal insulin deliveryprofiles or rates. For example, methods and systems provided herein mayanalyze projected blood glucose values for basal insulin deliveryprofiles or rates that last over the next four hours but repeat theprocess of selecting a new basal insulin delivery profile or rate everyfifteen minutes. In some cases, methods and systems provided herein candelay or suspend basal insulin delivery during the delivery of a bolus,which can be triggered by a user requesting a bolus.

As used herein, “basal insulin delivery” has its normal and customarymeaning within the art of the treatment of diabetes. Although basalrates are expressed as a continuous supply of insulin over time, basalinsulin delivery may constitute multiple discrete deliveries of insulinat regular or irregular intervals. In some cases, methods and systemsprovided herein may only be able to deliver insulin in discretefractions of a unit. For example, some insulin delivery devices can onlydeliver insulin in a dose that are an integer multiple of 0.05 unit or0.1 unit. In some cases, a delivery of basal insulin can include adelivery of insulin at predetermined time intervals less than or equalto fifteen minutes apart or less, ten minutes apart or less, or fiveminutes apart or less. In some cases, the time interval between discretebasal insulin deliveries can be determined based on the basal insulindelivery rate (e.g., a basal rate of 1.0 unit/hour might result in thedelivery of 0.1 unit every six minutes). As used herein, the term“bolus” has its normal and customary meaning with the art of thetreatment of diabetes, and can refer to a bolus delivered in order tocounteract a meal (i.e., a meal-time bolus) and/or to correct forelevated blood glucose levels (i.e., a correction bolus).

Methods and systems provided herein can in some cases include multipledelivery modes. In some cases, methods and systems provided herein canmonitor the presence of blood glucose using one or more blood glucosemeasuring devices or methods, control or monitor the dispensation ofmedicine, and determine and/or update the user-specific dosageparameters regardless of the operating mode. For example, possibleoperating modes could include closed-loop or hybrid closed-loop modesthat automatically adjust basal rates based on continuous glucosemonitoring data (CGM) and other user-specific dosage parameters (e.g.,baseline basal rate (BBR), insulin sensitivity factor (ISF), andcarbohydrate-to-insulin ratio (CR)), modes that can use blood glucosemonitor (BGM) data to update user-specific dosage parameters (e.g.,BBRs, ISFs, and CRs) for different time blocks over longer periods oftime, manual modes that require a patient to manually control thetherapy program using an insulin pump, and advisory modes that recommenddosages for a PWD to inject using an insulin pen or syringe. Bydetermining optimized control parameters that work across deliverymodes, systems and methods provided herein can provide superior analytecontrol even when a PWD switches to a different delivery mode. Forexample, methods and systems provided herein may be forced to switchaway from a hybrid closed-loop delivery mode that adjusts basal insulindelivery away from a BBR if a continuous glucose monitor malfunctions orthe system otherwise loses access to continuous data. In some cases,data can be collected when the system is in an advisory or manual modeto optimize control parameters in preparation for a PWD to switch to ahybrid closed-loop system (e.g., in preparation for a PWD to start useof a continuous glucose monitor (CGM) and/or an insulin pump).

Methods and systems provided herein can include an insulin pump and atleast one blood glucose measurement device in communication with theinsulin pump. In some cases, the blood glucose measurement device can bea CGM adapted to provide blood glucose measurements at least everyfifteen minutes. In some cases, methods and systems provided hereininclude a CGM adapted to provide blood glucose measurements at leastevery ten minutes. In some cases, methods and systems provided hereininclude a CGM adapted to provide blood glucose measurements every fiveminutes. Methods and systems provided herein additionally include acontroller adapted to determine an amount of basal insulin for deliveryto a PWD and memory to store multiple user-specific dosage parameters.In some cases, the controller can be part of an insulin pump. In somecases, a controller can be part of a remote device, which cancommunicate wirelessly with an insulin pump. In some cases, thecontroller can communicate wirelessly with a CGM. In some cases, methodsand systems provided herein can additionally include a user interfacefor displaying data and/or receiving user commands, which can beincluded on any component of a system provided herein. In some cases,the user interface can be part of smartphone. In some cases, a user caninput information on the user interface to trigger methods and systemsprovided herein to deliver a bolus of insulin. In some cases, methodsand systems provided herein can use a blood glucose meter adapted to usetest strip as a blood glucose measurement device. In some cases, methodsand systems provided herein can additionally include an insulin pen,which can optionally communicate wirelessly with a controller.

Setting Initial User-Specific Dosage Parameters

Systems and methods provided herein can use multiple user-specificdosage parameters for a PWD in order to determine rates of basal insulindelivery and optionally amounts of bolus insulin delivery. In somecases, initial user-specific dosage parameters can be set by ahealthcare professional. In some cases, data entered by a user (e.g.,the PWD, the PWD's caregiver, or a health care professional) can be usedto estimate one or more user-specific dosage parameters. For example,FIG. 11 depicts a method where a user enters at least one dosageparameter at block 252.

In some cases, multiple user-specific dosage parameters can be set formultiple diurnal time segments. In some cases, different user-specificdosage parameters can have diurnal time segments of the same length oftime or different lengths of time. In some cases, an initial setting foreach user-specific dosage parameter can be set at the same value foreach diurnal time segment, but the user-specific dosage parameter foreach diurnal time segment can be independently adjusted in the methodsand systems provided herein. In some cases, users (e.g., health careprofessionals) can input different user-specific dosage parameter valuesfor different diurnal time segments.

Methods and systems provided herein can, in some cases, useuser-specific dosage parameters that are commonly used in the treatmentof diabetes. For example, methods and systems provided herein can ask auser to input one or more of an average total daily dose (TDD) ofinsulin, a total daily basal (TDB) dose of insulin, an average basalrate (ABR) (which can be used as an initial baseline basal rate (BBR) inmethods and systems provided herein), an insulin sensitivity factor(ISF), and/or a carbohydrate-to-insulin ratio (CR). In some cases,methods and systems provided herein can ask for a weight, age, orcombination thereof of a PWD to estimate one or more user-specificdosage parameters. In some cases, methods and systems will store a BBR,an ISF, and a CR, which can each be set for multiple different timeblocks over a repeating period of time (e.g., fifteen, thirty, sixty, orone hundred and twenty minute diurnal periods). As will be discussed infurther detail below, methods and systems provided herein can adjustuser-specific dosage parameters for each of the diurnal periods in orderto personalize the delivery of insulin for the PWD in order to minimizerisks for the PWD.

Methods and systems provided herein can ask for or permit a user toinput a variety of different user-specific dosage parameters or dosageproxies to determine values for the initial settings of one or moreuser-specific dosage parameters and/or blood glucose targets. In somecases, the inputs can be limited to a Total Daily Basal (TDB) amount ofinsulin and a Fear of Hypoglycemia Index (FHI). In some cases, theinputs can be limited to a Total Daily Dose (TDD) amount of insulin anda FHI. In some cases, the TDB or TDD can be used determine the initialbaseline basal rate (BBR), the initial carbohydrate-to-insulin ratio(CR), and the initial insulin sensitivity factor (ISF) based onmathematical relationships among and between for BBR, CR, ISF, TDB, andTDD. In some cases, a user can also set an initial ISF and CR. In somecases, a user (e.g., a healthcare professional) can optionally input anycombination of BBR, CR, ISF, TDB, and TDD, and at least the initial BBR,initial CR, and initial ISF can be based on the values entered. Forexample, in some cases, a relationship between initial TDB, TDD, BBR,CR, and ISF can be expressed as follows: TDD [u/day]=2×TDB[u/day]=1800/ISF [mg/dL/u or [mmol/u]=400/CR [g/u]=48 hours/day×BBR[u/hour]. In some cases, the mathematical equation used to estimate ISF,CR, and BBR can use non-linear relationships between BBR, ISF, and CR.

Methods and systems provided herein can also make adjustments touser-entered user-specific dosage parameters prior to initial use. Insome cases, methods and systems provided herein adjust user enteredinitial BBR, CR, and/or ISF values based on mathematical relationshipsamong and between the initial BBR, CR, and ISF values. In some cases, ifan entered ISF and an entered CR are outside of a predefinedrelationship between BBR, CR, and ISF, methods and systems providedherein will calculate a CR and an ISF that meets a predeterminedrelationship between BBR, CR, and ISF while minimizing a total changefrom the entered values for ISF and CR. In some cases, the predeterminedrelationship permits a range of CR values for each ISF value, permits arange of ISF values for each CR value, and permits a range of ISF and CRvalues for each BBR value. In some cases, the predetermined relationshiprepresents a confidence interval for empirical data regardingrelationships between basal rates, ISF, and CR values for a populationof PWDs. In some cases, if an entered ISF, BBR, and/or CR are outside ofa predefined relationship between BBR, CR, and ISF, methods and systemsof the present disclosure may notify the user of the deviation from thepredefined relationship. Additionally or alternatively, a healthcareprovider override may be required to include ISF, BBR, and/or CR valuesoutside of the predefined relationship as the initial user-specificdosage parameters.

Setting Initial Blood Glucose Targets

Initial blood glucose targets can be set or determined using anysuitable technique. In some cases, blood glucose targets can bepreprogrammed on memory within a system or device provided herein. Insome cases, there can be a single blood glucose target preprogrammedinto the system that does not change. In some cases, the diurnal timesegments can each have a preprogrammed blood glucose target. In somecases, a user can program one or more blood glucose targets, which canbe set differently for different periods of time. In some cases, a usercan program the typical sleeping schedule, exercise schedule, and/ormeal schedule for a PWD, and methods and systems provided herein canselect lower blood glucose targets for sleep times and higher bloodglucose targets around meal times and/or exercise times. In some cases,historical continuous glucose monitor data for the PWD prior to the PWDusing the system can be used to set initial blood glucose targets(either for specific diurnal periods or for an entire day). In somecases, methods provided herein can have a PWD wear a CGM for apreliminary period of time (e.g., at least twenty-four hours, at leastforty-eight hours, at least five days, or at least ten days) prior toallowing the methods and systems provided herein from delivering insulinat rates other than the BBR to detect blood glucose variability data forthe PWD to set one or more initial blood glucose targets.

In some cases, such as shown in FIG. 11 at block 251, a user can enter afear of hypoglycemia index (FHI), which can be used to determine and/oradjust blood glucose targets. An FHI can be represented to the user in anumber of ways. In some cases, the FHI can be represented to the user asan aggressiveness index, which could be set at “prefer high,” “preferlow,” or “prefer moderate.” In some cases, the FHI can be represented tothe user as a target blood glucose level or target average blood glucoselevel (e.g., 100 mg/dl, 120 mg/dl, or 140 mg/dl). In some cases, the FHIcan be represented to the user as a target A1C level. In some cases, theFHI can be represented to the user as a probability of going above orbelow a certain threshold (e.g., a five percent chance of going below 80mg/dl, or a three percent chance of going above 200 mg/dl). In these andother cases, a user interface may be generated with an interactivefeature (e.g., radio buttons, check boxes, hyperlinked images/text,drop-down list, etc.) that a user can interact with to make a selectionof the FHI. In some cases, the PWD may interact with the interface toselect the FHI, and in some cases, the user can be a health careprofessional that selects the FHI.

In some cases, each possible FHI value can correspond to a preprogrammedinitial blood glucose target. For example, an FHI of “prefer high” mightcorrespond to a preprogrammed initial blood glucose target of 140 mg/dl,an FHI of “prefer moderate” might correspond to a preprogrammed initialblood glucose target of 120 mg/dl, and an FHI of “prefer low” mightcorrespond to a preprogrammed initial blood glucose target of 100 mg/dl.As will be discussed below, initial blood glucose targets can beadjusted over time based on data collected in methods and systemsprovided herein.

Modes of Operation

Methods and systems provided herein can in some cases include multipledelivery modes. In some cases, methods and systems provided herein canmonitor the presence of blood glucose using one or more blood glucosemeasuring devices or methods, control or monitor the dispensation ofinsulin, and determine and/or update the user-specific dosage parametersregardless of the operating mode. For example, possible operating modescould include closed-loop or hybrid closed-loop modes that automaticallyadjust basal rates based on continuous glucose monitoring data (CGM) andother user-specific dosage parameters (e.g., BBR, ISF, and CR), modesthat can use blood glucose monitor (BGM) data to update user-specificdosage parameters (e.g., BBRs, ISFs, and CRs) for different time blocksover longer periods of time, manual modes that require a patient tomanually control the therapy program using an insulin pump, and advisorymodes that recommend dosages for a user to inject using an insulin penor syringe. By determining optimized control parameters that work acrossdelivery modes, systems and methods provided herein can provide superiorblood glucose control even when a PWD switches to a different deliverymode. For example, methods and systems provided herein may be forced toswitch away from a hybrid closed-loop delivery mode that adjusts basalinsulin delivery away from a BBR if a continuous glucose monitormalfunctions or the system otherwise loses access to continuous data,yet still use a personalized ISF and CR for calculating correctionand/or mealtime bolus amounts. In some cases, data can be collected whenthe system is in an advisory or manual mode to optimize controlparameters in preparation for a PWD to switch to a hybrid closed-loopsystem (e.g., in preparation for a PWD to start use of a continuousglucose monitor (CGM) and/or an insulin pump). In some cases, the use ofa closed-loop delivery mode that adjusts basal insulin delivery awayfrom a BBR may be prevented until a sufficient amount of current bloodglucose data is available (e.g., the insulin delivery according tomultiple profiles that can occur at blocks 263, 264, 265, and 272 ofFIG. 11 may not occur until sufficient CGM and/or BGM data is collectedat the block 271 of FIG. 11). In some cases, systems and methodsprovided herein can deliver insulin at the BBR rate for each diurnalperiod when insufficient blood glucose data is available. In some cases,methods and systems provided herein can switch between open loop andclosed-loop modes based on whether there are a predetermined number ofauthenticated blood glucose measurements from a continuous glucosemonitor within a predetermined period of time (e.g., at least twoauthenticated blood glucose data points within the last twenty minutes).

Automating Basal Insulin Delivery

Systems and methods provided herein can automate basal insulin deliverybased on one or more stored user-specific dosage parameters (e.g., BBR,ISF, CR), one or more blood glucose targets, and/or blood glucose data.The example method depicted in FIG. 11 depicts an example process ofautomating basal insulin delivery as blocks 263, 264, 265, and 272.Methods and systems provided herein can use a model predictive controlsystem that projects multiple future blood glucose levels for a futuretime period for multiple possible basal insulin delivery profiles and/orrates over that future time period and determines which of the multiplepossible basal insulin delivery profiles and/or rates will producefuture blood glucose values that approximate one or more blood glucosetargets. Methods and systems provided herein can produce improvedcontrol as compared to control algorithms that merely make adjustmentsto basal insulin delivery without evaluating multiple possible basalinsulin delivery profiles or rates. In some cases, methods and systemsprovided herein can predict future blood glucose values at least twohours, or at least three hours, or at least four hours, or at least fivehours into the future, which can adequately consider the long termimpact of increasing or decreasing the basal insulin delivery relativeto the BBR. After a rate or profile is selected, the rate or profile canbe delivered for a predetermined delivery period of time (for example,the block 272 of FIG. 11) prior to repeating one or more of the steps inthe process of selecting a new basal insulin delivery profile or rate.In some cases, this predetermined delivery period of time can be lessthan the length of time for the generated basal insulin deliveryprofiles and/or rates and less than the time period for which futureblood glucose values were estimated, thus methods and systems providedherein can dynamically make changes to basal insulin delivery based onrecent blood glucose data. For example, generating basal deliveryprofiles at block 263 may be repeated every fifteen minutes, and theperiod of time evaluated at block 264 may be a four-hour window suchthat every fifteen minutes, a new four hour window of analysis for thebasal delivery profiles is generated. In this way, each delivery actionis based on a prediction not only of that action, but on how the priordelivery action is determined to impact blood glucose levels for fourhours into the future.

Generating Possible Basal Delivery Profiles and/or Rates for Evaluation

Possible basal insulin delivery profiles and/or rates can be generatedusing any suitable technique. In some cases, each generated profile orrate can be based on user-specific dosage parameters. In some cases,each generated profile or rate can be based on one or more user-specificdosage parameters that are specific to a particular diurnal period. Insome cases, each generated profile or rate is based on a predeterminedrelationship to a stored baseline basal rate (BBR), such as indicated atblock 263 in FIG. 11. In some cases, generated profiles and/or rates foranalysis extend for at least two hours, at least three hours, or for atleast four hours. In some cases, the generated profiles may extend for aday (e.g., twenty-four hours) or less. In some cases, each generatedprofile or rate includes basal insulin delivery rates based onpredetermined multiples or fractions of one or more stored BBRs. In somecases, multiple insulin delivery profiles and/or rates are based onmultiple diurnal-time-block-specific BBRs. In some cases, generatedbasal insulin delivery profiles each deliver insulin at a ratio of aBBR, such as an integer multiple of one or more stored BBRs (e.g.,0×BBR, 1×BBR, 2×BBR, and 3×BBR). In some cases, insulin deliveryprofiles can delivery insulin at ratios that may include both fractionsand multiples of one or more stored BBRs (e.g., 0×BBR, 0.5×BBR, 1×BBR,1.5×BBR, and 2×BBR). In some cases, generated basal insulin deliveryprofiles each deliver insulin at only multiples or fractions of between0 and 3. In some cases, generated basal insulin delivery profiles eachdeliver insulin at only multiples or fractions of between 0 and 2. Insome cases, multiple generated basal delivery profiles can include onlydeliveries of basal insulin at 0% of BBR, 100% of BBR, or 200% of BBR.In some cases, each generated basal delivery profile permutation hasfixed future time periods. In some cases, different future time periodsfor permutations can have different lengths. In some cases, the numberof generated basal delivery profiles or rates for evaluation is lessthan 100%, less than 50%, less than 30%, less than 25%, or less than20%. By limiting the number of evaluated preset permutations based onstored BBRs, methods and systems provided herein can limit an energyexpenditure used to run a controller determining a basal delivery rate.

In some cases, one or more of the profiles may include an inflectionpoint between a first insulin delivery amount for a first portion ofdelivery actions and a second delivery amount for a second portion ofdelivery actions. For example, a profile may include an inflection pointbetween 0% and 100% between 3.5 hours and 4 hours (e.g., for the portionbefore the inflection point, 0% of the BBR is delivered as the deliveryaction and for the portion after the inflection point, 100% of the BBRis delivered as the delivery action). As another example, anotherprofile may include an inflection point between 100% and 200% between 1hour and 1.5 hours (e.g., before the inflection point, 100% of the BBRis delivered as the delivery action and after the inflection point, 200%of the BBR is delivered as the delivery action). In some cases, eachprofile may be a permutation of including one inflection point (or noinflection point) between three possible delivery actions (e.g., 0%,100%, 200%). In some cases, more than one inflection point may be used,yielding additional profiles. In some cases, the number of profiles maybe fewer than thirty. In some cases, only three profiles are analyzed inorder to select between whether to delivery 0%, 100%, or 200%. In somecases, the inclusion of additional profiles assuming no basal insulin orcontinuing supply of maximum basal insulin can allow the system todetect an approaching predicted hypoglycemic event or an approachingpredicted hyperglycemic event, and additional profiles can be selectedand recorded to detect situations where future decisions are notconforming to an expected profile. In some cases, methods and systemsprovided herein can continue to deliver insulin according to a selectedprofile after the select period of time in block 272, including changesin basal delivery rates, if reliable up-to-date blood glucose data islost. In other cases, methods and systems provided herein will revert toanother mode or alarm and stop insulin delivery if reliable up-to-dateblood glucose data is lost.

In some cases, the range of possible values of the BBR for a givenprofile can be adjusted or modified depending on the FHI. For example,in some cases, if the FHI is “prefer low” (e.g., indicating a preferencefor the system to aggressively keep the PWD within range and not gohigh), the target blood glucose might be set around 100 mg/dl and therange for delivery may include 0%, 50%, 100%, 200%, and 300% BBR. Asanother example, if the FHI is “prefer high” (e.g., indicating that thePWD prefers to avoid hypoglycemic events even with a higher risk ofhyperglycemic events), the target blood glucose might be set around 140mg/dl and the range for delivery may include 0%, 100%, and 200% of BBR.

Evaluating Generated Basal Delivery Profiles and/or Rates

Referring again to FIG. 11, the evaluation of multiple generated basalinsulin delivery profiles and/or rates includes projecting future bloodglucose levels and comparing those to blood glucose targets. In somecases, multiple permutations may be generated and analyzed.

Predicting Future Blood Glucose Values

Systems and methods provided herein can use any suitable physiologymodel to predict future blood glucose values. In some cases, methods andsystems provided herein can predict future blood glucose values usingpast and current carbohydrate, insulin, and blood glucose values.

Systems and methods provided herein can in some cases estimate a firstfuture blood glucose a model as depicted in FIG. 19. In some cases,blood glucose can be approximated using two determinist Integratingfirst order plus dead time (FOPDT) models for the effect ofcarbohydrates and insulin, combined with an autoregressive (AR2)disturbance model. Accordingly, blood glucose (BG) at time (t) can beestimated using the following equation:BG_(t) =y _(t)=BGc _(t)+BGi _(t)+BGd _(t) =G _(c) c _(t) +G _(i) i _(t)+G _(d) e ^(a) ^(t)

From the equation above, the first element may represent the effect onblood glucose due to carbohydrates:

$G_{c} = \frac{{K_{c}\left( {1 - \alpha_{c}} \right)}B^{c_{dt}}}{\left( {1 - {\alpha_{c}B}} \right)\left( {1 - B} \right)}$where:

B is the backward shift operator such that BY_(t)=Y_(t-1),B²Y_(t)=Y_(t-2), B^(k)Y_(t)=Y_(t-k)

$k_{c} = \frac{ISF}{CR}$

is the carb gain (in units of mg/dl/g)

${\alpha_{c} = e^{- \frac{ts}{\tau_{c}}}},$

-   where τ_(c) is the carb time constant (for example, approximately 30    minutes), and-   where ts is the sampling time (for example, a CGM may use a sampling    time interval of every 5 minutes)    -   cat=floor(τ_(dc)/ts), where τ_(dc) is the carb deadtime (for        example, approximately 15 minutes)

From the equation above, the second element may represent the effect onblood glucose due to insulin:

$G_{i} = \frac{{K_{i}\left( {1 - \alpha_{c}} \right)}B^{i_{dt}}}{\left( {1 - {\alpha_{i}B}} \right)\left( {1 - B} \right)}$where: k_(i) = −ISF  is  the  insulin  gain  (in  units  of  mg/dl/unit)${\alpha_{i} = e^{- \frac{ts}{\tau_{i}}}},$

-   where τ_(i) is the insulin time constant (for example, approximately    120 minutes)    -   i_(dt)=floor(τ_(di)/ts), where τ_(di) is the insulin deadtime        (for example, approximately 30 minutes)

From the equation above, the third element may represent the effect onblood glucose due to disturbances (e.g., the AR2 disturbance model):G _(d) e ^(a) ^(t)and may be based on the following log-transformed AR2 model:

${\ln\left( \frac{{BGd}_{t}}{\mu^{*}} \right)} = {{\alpha_{1}{\ln\left( \frac{{BGd}_{t}}{\mu^{*}} \right)}} + {\alpha_{2}{\ln\left( \frac{{BGd}_{t - 2}}{\mu^{*}} \right)}} + \alpha_{t}}$which when rearranged, yields:BGd _(t)=BGd _(t-1) ^(α) ¹ BGd _(t-2) ^(α) ² μ*^((1-α) ¹ ^(-α) ² ⁾ e^(α) ^(t)where, in some examples,

a_(t)∼Normal(0, σ_(a)) and$\sigma_{a} \approx {50\%\mspace{14mu}{\ln\left( \sigma^{*} \right)}\sqrt{\left. {{\frac{1 + \alpha_{2}}{1 - \alpha_{2}}\left( \left( {1 - \alpha_{2}} \right)^{2} \right)} - \alpha_{1}^{2}} \right)}}$withµ^(*) ∼ 10^(Normal(2.09, 0.08))  and  σ^(*) ∼ 10^(Normal(0.15, 0.028))  such  thatα₁ ≈ 1.6442,  α₂ ≈ 0.6493.

Using the above notation, expansion of the initial equation for BG_(t)may be represented by the equation:

${BG}_{t} = {{\frac{k_{c}\left( {1 - \alpha_{c}} \right)}{\left( {1 - {\alpha_{c}B}} \right)\left( {1 - B} \right)}c_{t - {dt}_{c}}} + {\frac{k_{i}\left( {1 - \alpha_{i}} \right)}{\left( {1 - {\alpha_{i}B}} \right)\left( {1 - B} \right)}i_{t - {dt}_{i}}} + {{BGd}_{t - 1}^{\alpha_{1}}{BGd}_{t - 2}^{\alpha_{2}}\mu^{*{({1 - \alpha_{1} - \alpha_{2}})}}}}$

Systems and methods provided herein can in some cases calculate anamount of insulin on board (IOB) and/or an amount of carbohydrates onboard (COB) in order to predict future blood glucose values. IOB and COBrepresent the amount of insulin and carbohydrates, respectively, whichhave been infused and/or consumed but not yet metabolized. Knowledge ofIOB and COB can be useful for a user of a method or system providedherein when it comes to bolus decisions to prevent insulin stacking, butknowledge of IOB and COB can also be used in methods and systemsprovided herein to predict future blood glucose values.

IOB and COB represent the amount of insulin and carbohydrates,respectively, which have been infused and/or consumed but not yetmetabolized. Knowledge of IOB can be useful in correcting bolusdecisions to prevent insulin stacking. Knowledge of IOB and COB can beuseful for predicting and controlling blood glucose. Both insulininfusion and carbohydrate consumption can involve deadtime ortransportation delay (e.g., it can take ten to forty minutes for insulinand/or carbohydrates to begin to affect blood glucose). During theperiod immediately after entering the body (e.g., during the deadtimeperiod), it can be beneficial to account for IOB and COB in anydecisions such as bolusing. This can be called “Decision” IOB/COB.“Action” IOB/COB, on the other hand, can represent the insulin and/orcarbohydrates available for action on blood glucose. In some cases,Decision IOB can be a displayed IOB, while Action IOB can be an IOBdetermined for use in selecting a basal delivery rate or profile inmethods and systems provided herein.

From the equations above,

${BG}_{it} = {\frac{{- {{ISF}\left( {1 - \alpha_{i}} \right)}}B^{i_{dt}}}{\left( {1 - {\alpha_{i}B}} \right)\left( {1 - B} \right)}i_{t - i_{dt}}}$where BY_(t) = Y_(t − 1),  B²Y_(t) = Y_(t − 2),  B^(k)Y_(t) = Y_(t − k)$\alpha_{i} = e^{- \frac{ts}{\tau_{i}}}$

-   where τ_(i) is the insulin time constant (for example, approximately    120 minutes)    -   i_(dt)=floor(τ_(di)/ts), where τ_(di) is the insulin deadtime        (for example, approximately 30 minutes) and where ts is the        sampling time (for example, a CGM may use a sampling time        interval of every 5 minutes)

“Decision” IOB

In some embodiments, Decision IOB at time (t) (IOB_D_(t)) may becalculated according to the following mathematical process:

${IOB\_ D}_{t} = {{IOB\_ D}_{t - 1} - \frac{{BGi}_{t} - {BGi}_{t - 1}}{- {ISF}} + i_{t}}$or, alternatively,

${\nabla{IOB\_ D}_{t}} = {{- \frac{\nabla{BGi}_{t}}{- {ISF}}} + i_{t}}$substituting the equation above for Bat into the equation for IOB_D_(t)or ∇IOB_D_(t) yields

${IOB}_{D_{t}} = {\frac{1 - {\alpha_{i}B} - {\left( {1 - \alpha_{i}} \right)B^{i}{dt}}}{1 - {\left( {\alpha_{i} + 1} \right)B} + {\alpha_{i}B^{2}}}i_{t}}$or, alternatively,

${\nabla{IOB\_ D}_{t}} = {{{- \frac{1 - \alpha_{i}}{1 - {\alpha_{i}B}}}i_{t - i_{dt}}} + i_{t}}$

“Action” IOB

In some embodiments, Action IOB at time (t) (IOB_A_(t)) may becalculated according to the following mathematical process:

${IOB\_ A}_{t} = {\frac{1}{1 - {\alpha_{i}B}}i_{t - i_{dt}}}$

For an arbitrary series of insulin infusions, using an infinite seriesof expansions

-   -   of

$\frac{1}{1 - {\alpha_{i}B}},{IOB\_ A}_{t}$may be represented by

${IOB}_{A_{t}} = {\sum\limits_{k = 0}^{n}\;{\alpha_{i}^{k}i_{t - k - i_{dt}}}}$

Stated another way,

${BGi}_{t} = {\frac{- {{ISF}\left( {1 - \alpha_{i}} \right)}}{1 - B}{IOB\_ A}_{t}}$

The formulas for COB, including Action COB and Decision COB, may bedeveloped in a similar fashion, using the equation above related toG_(c):

$G_{ct} = \frac{{k_{c}\left( {1 - \alpha_{c}} \right)}B^{c_{dt}}}{\left( {1 - {\alpha_{c}B}} \right)\left( {1 - B} \right)}$

Accordingly, future blood glucose data can be estimated using current orrecent blood glucose data, data about when carbohydrates were consumed,and/or data regarding when insulin was and/or will be administered.Moreover, because evaluated insulin delivery profiles and/or ratesinclude basal insulin delivery rates above and below the BBR, thoseinsulin delivery rates above BBR can be added to the IOB calculation andinsulin delivery rates below the BBR can be subtracted from the IOB. Insome cases, a variation in a Decision IOB due to actual variations fromBBR can be limited to positive deviations in order to prevent a userfrom entering an excessive bolus.

Estimating Glucose Levels from Blood Glucose Data

Referring back to FIG. 1, continuous glucose monitor 50 and bloodglucose meter 70 can both provide blood glucose data to system 10. Theblood glucose data, however, can be inaccurate. In some cases,continuous glucose monitor 50 can be replaced (or have sensor shaft 56replaced) at regular or irregular intervals (e.g., every three days,every five days, every seven days, or every ten days). In some cases,data from blood glucose meter 70 can be used to calibrate the continuousglucose monitor 50 at regular or irregular intervals (e.g., every threehours, every six hours, every twelve hours, every day, etc.). In somecases, systems and methods provided herein can remind a user to changethe continuous glucose monitor 50 or calibrate continuous glucosemonitor 50 using blood glucose meter 70 based on data from continuousglucose monitor 50 and/or at regular intervals. For example, if thepattern of insulin delivery varies greatly from an earlier predictedpattern of insulin deliveries may indicate that the continuous glucosemonitor 50 requires maintenance and/or replacement.

In some cases, methods and systems can determine an accuracy factor forblood glucose data from the continuous glucose monitor 50 based uponwhen the particular continuous glucose monitor sensor shaft 56 was firstapplied to the PWD and/or when the particular continuous glucose monitor50 was last calibrated using blood glucose data from blood glucose meter70. In some cases, methods and systems provided herein make adjustmentsto future blood glucose targets based on a calculated accuracy factorfor data from the continuous glucose monitor 50 in order to reduce arisk of hypoglycemia. In some cases, methods and systems provided hereincan estimate the current blood glucose level as being a predeterminednumber of standard deviations (e.g., 0.5 standard deviation, onestandard deviation, two standard deviations) below data received fromcontinuous glucose monitor 50 based on the accuracy factor in order toreduce a risk of hypoglycemia.

After continuous glucose monitor 50 is calibrated or replaced with a newcontinuous glucose monitor or has a new sensor shaft 56 installed,however, a discontinuity of reported glucose data from the continuousglucose monitor 50 can occur. In some cases, methods and systemsprovided herein, however, can use and report historical blood glucosevalues in selecting insulin basal rates and/or profiles. In some cases,methods and systems provided herein can revise stored and/or reportedblood glucose levels based on data from one or more continuous glucosemonitors in order to transition between different continuous glucosemonitor sensors and/or to data produced after a calibration. In somecases, a continuous glucose monitor 50 can provide each blood glucosevalue with an estimated rate of change, and the rate of changeinformation can be used to retrospectively adjust one or more historicalestimated blood glucose values from data from a continuous glucosemonitor 50. For example, the rate of change of the pre-calibrationreported blood glucose value may be used to determine an estimatedpost-calibration value assuming the same rate of change. A ratio of thepost-calibration reported blood glucose value to the estimatedpost-calibration value can then be used to linearly interpolate multiplehistorical blood glucose values based on that ratio. In some cases, allreadings between calibrations can be linearly interpolated. In somecases, data from a predetermined amount of time prior to a calibrationcan be linearly interpolated (e.g., fifteen minutes, thirty minutes, onehour, two hours, three hours, or six hours).

Analyzing Variations from Targets

Methods and systems provided herein can evaluate each future basaldelivery profile by predicting blood glucose for the basal deliveryprofiles and calculating a variation index of the predicted bloodglucose values from the target blood glucose values. Methods and systemsprovided herein can then select the profile of basal rate deliveryactions that corresponds to the lowest variation index. The variationindex can use a variety of different mathematical formulas to weightdifferent types of variations. The variation index can be a costfunction. In some cases, methods and systems provided herein can use acost function that sums up squares of differences for the projectedblood glucose values from target blood glucose values for multiplediurnal time segments. Methods and systems provided herein can use anysuitable cost function. In some cases, cost functions can sum theabsolute value of the difference between each predicted blood glucosevalue and each blood glucose target. In some cases, cost functions usedin methods and systems provided herein can use a square of thedifference. In some cases, cost functions used in methods and systemsprovided herein can use a square of the difference between the logs ofeach predicted blood glucose level and each corresponding blood glucosetarget. In some cases, cost functions used in methods and systemsprovided herein can assign a higher cost to blood glucose values belowthe blood glucose target in order reduce the risk of a hypoglycemicevent. In some cases, a profile that has the lowest value of loss may beselected. In some cases, cost functions provided herein can includeelements that additional bias the selected profile toward a profile thatmaintains the previously administered basal rate and/or that deliversthe baseline basal rate, which may prevent the system from changingdelivery rates every time a basal delivery profile or rate is selectedin block 265, as shown in FIG. 11. In some cases, the cost function cansquare the difference between the log of the values in order to providea higher cost for projected lows than projected highs.

Selecting a Basal Insulin Delivery Profile or Rate

Methods and systems provided herein can then select a basal profile orrate that produces the lowest cost function value. With reference toFIG. 11, at block 272 insulin can then be delivered according to theselected profile for an amount of time. In some cases, the amount oftime is a predetermined amount of time. In some cases, the predeterminedamount of time is less than the time horizon for the estimated futureblood glucose values and the length of time for the selected basaldelivery profile. In some cases, the predetermined amount of time isninety minutes or less, sixty minutes or less, forty-five minutes orless, thirty minutes or less, twenty minutes or less, fifteen minutes orless, ten minutes or less, or five minutes or less. After the period oftime, the system can again repeat the operations at blocks 263, 264,265, and 272 to select and deliver a basal insulin for a subsequentperiod of time.

Adjusting User-Specific Dosage Parameters

Methods and systems provided herein can make adjustments to theuser-specific dosage parameters. For example, FIG. 11 includes the block281 for detecting time periods when an amount of delivered basal insulinis different from a BBR, which can then be used to adjust user-specificdosage parameters at block 262. These updated user-specific dosageparameters can then be used to generate new basal delivery profiles atblock 263 and used at block 264 to evaluate different basal deliveryprofiles. For example, for a BBR of 1.46 Units/hour (associated with aTDB of 35 Units/day), if a diurnal period under consideration is onehour and for the first forty-five minutes, insulin was delivered at arate of 2.92 Units/hour (e.g., 2× the BBR) and only the last fifteenminutes was delivered at a rate of 1.46 Units/hour (e.g., 1× the BBR),user-specific dosage parameters for a related diurnal time period (e.g.,that same hour on another day in the future, or a preceding diurnal timeperiod on a day in the future) may be adjusted.

In some cases, methods and systems provided herein can make adjustmentsfor BBR, ISF, and/or CR for multiple diurnal periods based on variationsin the insulin amounts actually delivered for that diurnal periodcompared to the baseline basal insulin rate for that diurnal period. Insome cases, diurnal periods can have a same length of time as apredetermined length of time for the delivery of a selected insulindelivery. In some cases, a diurnal period can be greater than apredetermined length of time for the delivery of a selected insulindelivery, for example, multiple doses of insulin may be delivered duringa single diurnal period. In some cases, a diurnal period can be fifteenminutes, thirty minutes, one hour, two hours, etc. In some cases, anactual delivery of insulin for a diurnal period must surpass apredetermined threshold above or below the BBR for that diurnal periodin order for user-specific dosage parameters (e.g., BBR, ISF, CR) to beadjusted for that diurnal period. For example, diurnal periods can beone hour long, but each basal delivery profile can be delivered forfifteen minute time periods before methods and systems provided hereindetermine a new basal insulin delivery profile, and methods and systemsprovided herein can require that the total basal insulin delivery forthe diurnal period be at least greater than 50% of the BBR to increasethe BBR for that diurnal period or at 50% or less than the BBR todecrease the BBR for that diurnal period. Using the example from above,for a BBR of 1.46 Units/hour, if a diurnal period under consideration isone hour and for the first forty-five minutes (e.g., three iterations ofprofile generation and delivery actions), insulin was delivered at arate of 2.92 Units/hour (e.g., 2× the BBR) and only the last fifteenminutes (e.g., one iteration of profile generation and delivery action)was delivered at a rate of 1.46 Units/hour (e.g., 1× the BBR), the totalamount delivered would be at 175% of the BBR for the one hour diurnalperiod, or an average ratio of 1.75× the BBR. In some cases, because the175% exceeded 150% of the BBR, methods and systems of the presentdisclosure may adjust user-specific dosage parameters. As anotherexample using the same 1.46 Units/hour BBR and a two hour diurnal timeperiod and delivery profiles reformulated every fifteen minutes, if thefirst forty-five minutes delivered no insulin (0× the BBR) and the lasthour and fifteen minutes delivered 1.46 Units/hour, the total amountdelivered may be 62.5% of the BBR, or 0.625× of the BBR. In some cases,because the 62.5% did not drop below 50% of the BBR, methods and systemsof the present disclosure may not adjust the user-specific dosageparameters and may maintain the user-specific dosage parameters for theparticular diurnal period.

An adjustment to the CR, ISF, and BBR can be any suitable amount. Insome cases, the adjustment to the BBR is less than the differencebetween the delivered basal insulin and the previously programmed BBR.In some cases, a change to each user-specific dosage parameter (e.g.,BBR, ISF, and CR) is at a predetermined percentage or value. Forexample, in some cases, each of BBR and ISF can be increased by 5%, 3%,or 1% and CR decreased by the same percent for every period where theamount of delivered basal insulin exceeds the BBR by at least 25%. Insome cases, BBR and ISF can be decreased by 5%, 3%, or 1% and CRincreased by the same percent for every period where the amount ofdelivered basal insulin exceeds the BBR by at least 25%. By setting eachadjustment at a low level, methods and systems provided herein caneventually be personalized for the PWD without over adjusting the systembased on an unusual day (e.g., to mitigate the risk of short termdisturbances being mistaken for changes in physiological parameters). Insome cases, the adjustment to CR, ISF, and BBR may be based on arelationship between CR, ISF, and BBR, rather than a fixed amount orpercentage. In some cases, CR, ISF, and BBR can be adjusted based on apredetermined relationship between their log-transformed values. In somecases, the adjustments to CR, ISF, and BBR may be performedindependently. In these and other cases, systems and methods providedherein can monitor for variations in adjustments to CR, ISF, and/or BBRaway from a relationship between CR, ISF, and BBR. In such cases, anotification may be provided to a user (e.g., the PWD or a health careprovider) that the systems and methods of the present disclosure hadadjusted one or more user-specific dosage guidelines outside of or awayfrom a relationship between CR, ISF, and BBR.

In some cases, systems and methods provided herein can update or adjustuser-specific operating parameters for select time blocks everytwenty-four hours. In some cases, diurnal periods can be updateddynamically (e.g., immediately after a basal delivery profile or rate isselected). In some cases, diurnal periods can be updated by reusablepump controller 200, by mobile computing device 60, or using a remoteserver in the cloud. In some cases, the length of diurnal periods canvary depending on the time of day (e.g., nighttime diurnal periods couldbe longer) or depending on the user-specific dosage parameter (e.g.,BBRs might have fifteen minute diurnal periods while the CR and ISFmight have one hour diurnal periods).

In some cases, when performing an adjustment, a related diurnal periodmay be adjusted based on variation from the BBR for a given diurnalperiod. For example, if an adjustment were to be performed becausedelivery from 2 PM to 3 PM exceeded 150% of the BBR, an adjustment maybe made to the user-specific dosage parameters for the same time on adifferent day in the future (e.g., 2 PM to 3 PM on the next day) or apreceding diurnal period on a different day in the future (e.g., 1 PM to2 PM on the next day or 12 PM to 1 PM on the next day, etc.). In somecases, modifying a preceding diurnal period may adjust moreappropriately for variations in BBR and/or other user-specific dosageparameters because of the delay of effect after delivery of insulinand/or the delay of effect after consumption of carbohydrates (e.g., ifa PWD repeatedly goes high between 2 PM and 3 PM, the PWD may needadditional insulin during the 1 PM to 2 PM hour).

In some cases, systems and methods disclosed herein can smoothadjustments to user-specific dosage parameters in one diurnal periodrelative to other diurnal periods. For example, in some cases, aproposed adjustment to a BBR for a first diurnal period may be comparedto one or more preceding diurnal periods and one or more followingdiurnal periods. If the proposed adjustment is a threshold amountdifferent from one or more of the preceding or following diurnal periodvalues, the proposed adjustment may be modified to avoid drastic jumpsbetween diurnal periods. For example, if a preceding diurnal period hada BBR of 1.06 Units/hour and the proposed adjustment was from a BBR of1.4 Units/hour to a BBR of 1.90 Units/hour, the adjustment may bereduced to smooth the transition from the preceding diurnal time period.In some cases, the smoothing may include adjusting preceding orfollowing diurnal time periods in addition to the diurnal time periodunder consideration. In these and other cases, such adjustment may beperformed once per day or at another periodic time such that followingdiurnal periods may have already occurred and the smoothing is not beingperformed based on projections. For example, the diurnal period from 1PM to 2 PM may be analyzed for potential adjustment at 4 PM such thatthe diurnal periods from 11 AM to 12 PM and 12 PM to 1 PM and from 2 PMto 3 PM and 3 PM and 4 PM are available in considering any adjustmentand/or smoothing to perform on the user-specific dosage parameters forthe 1 PM to 2 PM diurnal period.

In some cases, systems and methods disclosed herein can adjustuser-specific dosage parameters in a diurnal period based on the FHI.For example, if the FHI is high (e.g., indicating a preference that thePWD not go low), the range for adjusting the BBR may be limited to arelatively small change (e.g., 0.5%, 1%, 1.5%, etc.). As anotherexample, if the FHI is low (e.g., indicating that the PWD is not asconcerned about going low), the range for adjusting the BBR may includea broader range of changes (e.g., up to a 5% change).

Adjusting Blood Glucose Targets

Methods and systems provided herein can make adjustments to the bloodglucose targets. For example, FIG. 11 includes the block 283 foranalyzing the variability of CGM and/or BGM data (e.g., data from theCGM 50 and/or the BGM 70 of FIG. 1), which can then be used to adjustblood glucose targets at the block 261. In some cases, blood glucosetargets are set for diurnal periods. In some cases, the diurnal periodsfor blood glucose targets are at least fifteen minutes long, at leastthirty minutes long, at least one hour long, or at least two hours long.In some cases, blood glucose targets can have a constrained range. Insome cases, blood glucose targets must be at least 80 mg/dL, at least 90mg/dL, at least 100 mg/dL, at least 110 mg/dL, or at least 120 mg/dL. Insome cases, blood glucose targets must be no greater than 200 mg/dL, nogreater than 180 mg/dL, no greater than 160 mg/dL, no greater than 140mg/dL, or no greater than 125 mg/dL. In some cases, a constrained rangeis between 100 mg/dL and 160 mg/dL. These updated blood glucose targetscan then be used at block 264 to evaluate different basal deliveryprofiles.

Updated blood glucose targets for a particular diurnal period can bebased on historical blood glucose patterns for the PWD and the risk ofhypoglycemia for the PWD over the course of a day. The updated bloodglucose targets can also consider a set FHI. For example, based on anFHI selection, an initial blood glucose target at a conservative level(e.g., 120 mg/dl) can be set, and over the course of a period of daysand/or weeks as more information is gained about variability patterns,the blood glucose target(s) can be adjusted. A slow adjustment canprevent the blocks 283 and 261 from overreacting to short termdisturbances but still allow blood glucose target individualization to aPWD's lifestyle and habits over time.

In some cases, methods and systems provided herein can also allow a userto temporarily or permanently adjust blood glucose targets by adjustingtheir fear of hypoglycemia index (FHI). In some cases, a user adjustmentto FHI can result in blood glucose targets being temporarily orpermanently adjusted to blood glucose targets based on the variabilityof CGM (and optionally BGM) data for multiple diurnal periods. In somecases, a user adjustment to FHI can add or subtract a predeterminedvalue from a previously used blood glucose target (e.g., an adjustmentfrom “prefer low” to “prefer medium” could add 20 mg/dL to each storedblood glucose target). In some cases, a temporary adjustment to FHIcould analyze variability data for multiple time blocks and set a newblood glucose target for each diurnal period based on the variabilitydata for that time block (e.g., an adjustment from “prefer low” to“prefer medium” could adjust the blood glucose target for each diurnalperiod from a level estimated to send the PWD below a threshold of 70mg/dL about 5% of the time to a level estimated to send the PWD below athreshold of 70 mg/dL about 3% of the time).

Allowing a PWD to change the FHI for temporary time periods or otherwiseuse some form of temporary override may allow a PWD to tell the systemthat the PWD is about to or is experiencing some activity or conditionthat might impact their blood glucose levels. For example, a PWD that isabout to exercise might set a temporary FHI of “prefer high” to offsetthe risk that exercise will send the PWD low for that period of time. Insome cases, a PWD might set a temporary FHI of “prefer low” if the PWDis feeling sick in order to offset the risk that the sickness willresult in high blood glucose levels. In some embodiments, such atemporary override may be a separate setting or entry other than theFHI. In these and other cases, in addition to a preferred range (e.g.,“high” or “low”), the user may be able to select a temporary override ofa target blood glucose level or range (e.g., approximately 120 mg/dL orbetween 120 mg/dL and 200 mg/dL, etc.), or may select a particularactivity or circumstance the PWD will participate in or is experiencing(e.g., exercising, sickness, menses, driving, etc.).

In some cases, after a temporary override is input, methods and systemsof the present disclosure can select a new profile to follow based onthe profile more closely aligning with the temporary override. In theseand other cases, a new set of profiles can be generated before selectingthe new profile. Additionally or alternatively, after a temporaryoverride is input, methods and systems of the present disclosure cantemporarily modify the BBR. In some cases, after the BBR has beenmodified, a new set of profiles may be generated based on thetemporarily modified BBR.

In some cases a log of temporary overrides can be generated. Forexample, each time a user (e.g., the PWD) inputs an override, an entrycan be created in the log that includes what override was selected, whatstarting and ending times, and/or what the reason for the override was.In these and other cases, the log can be periodically provided to ahealthcare professional, for example, via email or some other electronicmessage. Additionally or alternatively, in some cases the log can beparsed for patterns. For example, the PWD may input a temporary overrideevery Monday, Wednesday, and Friday from 6 PM to 7 PM when the PWDexercises. The log can be parsed to find such patterns of overrides. Inthese and other cases, methods and systems of the present disclosure canmodify a BBR based on the patterns. Continuing the example, the BBR maybe lowered for the diurnal period of 6 PM to 7 PM on Monday, Wednesday,and Friday because of a PWD repeatedly entering a temporary overrideduring that diurnal period that the PWD is exercising and not to go low.

Overall System

Methods and systems provided herein can control basal insulin deliveryover time and adjust basal user-specific dosage parameters and bloodglucose targets for multiple diurnal periods to personalize theuser-specific dosage parameters over time. For example, FIG. 20illustrates various examples of user interfaces (e.g., X-400, X-410,X-420, and X-430) displaying various aspects of the present disclosure.

In some cases, FIG. 20 illustrates a simulation of a method providedherein, showing how methods and systems provided herein may generate auser interface X-400 that may illustrate BBRs X-401, CRs X-402, ISFsX-403, and blood glucose targets X-404 set for multiple time blocks. Forexample, after a system (e.g., the system 10 of FIG. 1) has run on a PWDafter thirty days, user-specific dosage parameters may be personalizedbased on adjustments made to the user-specific dosage parameters. Forexample, the user interface X-400 may align the various user-specificdosage parameters over various diurnal periods throughout a day. Forexample, the BBR X-401 may be higher around meal times (e.g., nine AM,twelve PM, and seven PM), and lower while the PWD is sleeping (e.g.,eleven PM to five AM). As an additional example, the CR X-402 and ISFX-403 may follow a similar trajectory of variation as illustrated forthe BBR X-401.

In some cases, as illustrated in user interface X-410 of FIG. 20,methods and/or systems of the present disclosure (including, forexample, back-end computer systems) may monitor and/or track bloodglucose levels over time. For example, the user interface X-410 mayillustrate glucose levels for one hundred and eighty days, with a barindicating the last thirty days. In some cases, when adjustinguser-specific dosage parameters, methods and systems of the presentdisclosure may disregard readings older than thirty days, or may weightmore recent readings more heavily than older readings.

In some cases, the user interface X-420 may include time aligned charts(including chart X-421, chart X-422, and chart X-423) that can show asix hour window of the timeline illustrated in user interface X-410. Asillustrated in FIG. 20, chart X-421 depicts the current blood glucosevalues as well as the predictions that have been made over time for thatparticular delivery time. For example, once the “current” bar X-424 isreached, there may have been multiple predictions made for each timesegment. As the window extends further into the future, the number ofpredictions may be lower. The chart X-422 illustrates the calculated IOBand the calculated COB for the PWD. The chart X-423 indicates whetherthe method or system delivered 0% of the BBR, 100% of the BBR, or 200%of the BBR for fifteen minute time segments.

As illustrated in FIG. 20, the user interface X-430 depicts a possibleuser interface for a PWD showing some data that may be displayed on amobile device of a PWD (e.g., the mobile computing device 60 of FIG. 1).In some cases, only the data prior to bar X-424 (e.g., historic data)may be shown in the user interface X-430. In a first section X-431 ofthe user interface X-430, historic blood glucose data can be displayed.In a second section X-432, announced meals and bolus insulin deliveriescan be displayed. In a third section X-433, the rates of basal deliverycan be displayed. The third section X-433 can differ from chart X-423 bydisplaying the actual rates of basal delivery rather than a ratio of therate delivered to the BBR. Section X-434 can display a current bloodglucose reading, a current IOB, and/or an indication of whether thesystem is automating. In some cases, more or less information can bedisplayed on the user interface X-430 than illustrated in FIG. 20. Forexample, the user interface X-430 may include any of the informationfrom user interfaces X-400, X-410, and/or X-420 in any combination.

Additional Details about Example Pump Assembly

FIG. 21 illustrates a flow diagram of an example method X-600 of usinginsulin delivery profiles. The method X-600 may be performed by anysuitable system, apparatus, or device. For example, the system 10, thepump assembly 15, the mobile computing device 60 of FIG. 1, and/or aremote server may perform one or more of the operations associated withthe method X-600. Although illustrated with discrete blocks, the stepsand operations associated with one or more of the blocks of the methodX-600 may be divided into additional blocks, combined into fewer blocks,or eliminated, depending on the desired implementation.

At block X-610, a set of insulin delivery profiles can be generated,each having a series of insulin delivery actions. For example, the pumpassembly 15 may generate a series of potential delivery actions that mayinclude permutations based on one or more potential inflection points inthe delivery actions.

At block X-620, a prediction can be made of future blood glucose levelsfor each of the delivery profiles. For example, the pump assembly 15and/or the mobile computing device 60 of FIG. 1 can generate aprediction of future blood glucose levels at various points in time if aparticular profile is followed. Such prediction may be based on theeffect of glucose, insulin, carbohydrates, and/or other disturbancesprojected for the blood glucose levels at the various points in time.

At block X-630, a determination can be made as to variations from atarget blood glucose level for each of the profiles. For example, thepump assembly 15 and/or the mobile computing device 60 of FIG. 1 maycompare the predicted blood glucose levels to a target blood glucoselevel for each of the various points in time. In some cases, the targetblood glucose level may be constant and in other cases, the target bloodglucose level may vary over time. In these and other cases, thevariation may be measured as a distance between the target blood glucoselevel and the projected blood glucose level, or a square of thedifference, etc., as described above.

At block X-640, the profile that approximates the target blood glucoselevel can be selected. In some cases, the profile that minimizesvariation from the target blood glucose level may be selected. Forexample, a cost function can be utilized and the profile with the lowestcost can be selected as the profile that approximates the target bloodglucose level.

At block X-650, insulin may be delivered based on the next action in theselected profile. For example, control circuitry 240 of the pumpassembly 15 may send a message to the pump portion of the pump assembly15 to deliver insulin based on the next action in the selected profile.For example, a next action may indicate that the pump is to deliver 0×,1×, or 2× of a BBR. The next action can be the first delivery action inthe set of actions of the profile.

In some cases, after the block X-650, the method X-600 can return to theblock X-610 to generate another set of insulin delivery profiles,predict future blood glucose levels, determine variations from a targetblood glucose level, etc. In some cases, the method X-600 can beperformed iteratively each time a PWD is to receive a dose of basalinsulin. In these and other cases, the method X-600 can routinely updatedelivery actions based on a repeatedly updated projection of the bloodglucose levels of the PWD and the effect a particular delivery actionmay have on the blood glucose levels. In some cases, methods and systemsprovided herein can change modes if there is a lack of reliable CGM dataat this point in time (e.g., the system can change modes to a mode whereBBR is delivered and potentially provide notice that the system hasexited the automation mode).

Modifications, additions, or omissions may be made to the method X-600without departing from the scope of the present disclosure. For example,the operations of the method X-600 may be implemented in differingorder. Additionally or alternatively, two or more operations may beperformed at the same time. Furthermore, the outlined operations andactions are provided as examples, and some of the operations and actionsmay be optional, combined into fewer operations and actions, or expandedinto additional operations and actions without detracting from theessence of the disclosed embodiments.

FIG. 22 illustrates a flow diagram of an example method X-700 ofadjusting insulin delivery rates. The method X-700 may be performed byany suitable system, apparatus, or device. For example, the system 10,the pump assembly 15, the mobile computing device 60 of FIG. 1, and/or aremote server may perform one or more of the operations associated withthe method X-700. Although illustrated with discrete blocks, the stepsand operations associated with one or more of the blocks of the methodX-700 may be divided into additional blocks, combined into fewer blocks,or eliminated, depending on the desired implementation.

At block X-710, insulin can be delivered over a diurnal time period. Forexample, the pump assembly 15 of FIG. 1 can deliver insulin to a PWDbased on a BBR for the diurnal time period. In some cases, the insulinmay be actually delivered at multiple points in time throughout thediurnal time period as a ratio of the BBR, such as 0×, 1×, and 2×.

At block X-720, variations between actual insulin delivered values andthe BBR for the diurnal time period can be determined. For example, ifthe delivery actions throughout the diurnal time period deliver a ratioof the BBR, the actual delivery actions may be averaged over the diurnaltime period to find an average ratio for the diurnal time period. Inthese and other cases, the actual insulin delivered values can be basedon periodically projected blood glucose levels and the BBR. For example,a set of insulin delivery profiles can be generated and a deliveryaction selected as described in the present disclosure (e.g., asdescribed in FIG. 21).

At block X-730, a determination is made as to whether the variationsbetween the actual insulin delivered values and the baseline basalinsulin rate exceeds a threshold. If the variations do exceed thethreshold, the method X-700 may proceed to the block X-740. If thevariations do not exceed the threshold, the method X-700 may proceedback to the block X-710. In some cases, the threshold may be based on aratio of the baseline basal delivery rate. For example, the thresholdmay include that the average rate over the diurnal period be above 150%of the BBR or below 50% of the BBR for the actual delivery values overthe diurnal time period.

At block X-740, the baseline basal insulin rate can be adjusted for arelated diurnal time period. For example, the BBR can be adjusted higherby a certain amount (e.g., 1%, 2%, or 5%) if the variations went above athreshold and can be adjusted lower by a certain amount (e.g., 1%, 2%,or 5%) if the variations went below a threshold. In some cases, therelated diurnal time period can be the same block of time (e.g., if thevariations exceeded the threshold during the 2 PM to 3 PM diurnalperiod, then the BBR from 2 PM to 3 PM of the next day may be adjusted)on another day in the future, and in some cases, the related diurnaltime period can be a different time on another day (e.g., if thevariations exceeded the threshold during the 2 PM to 3 PM diurnalperiod, then the BBR from 1 PM to 2 PM of the next day may be adjusted).In some cases, such an adjustment may be performed once per day for allthe diurnal periods of that day.

In some cases, the adjustment at block X-740 can include smoothing ofthe adjustment. For example, a potential modification can be compared tothe BBR of the preceding diurnal time period or the following diurnaltime period, and may modify the adjustment to be closer to the otherdiurnal time periods. Additionally or alternatively, the BBR can besmoothed by comparing the potential modification to BBRs of the sametime of day for preceding days to determine whether the potentialmodification may be responsive to an unusual day.

In some cases the adjustment at block X-740 can consider other factors.For example, the adjustment can be based on penalizing a modificationthat increases the probability of the PWD having a hypoglycemic event(e.g., by penalizing modifications that may increase the probability ofthe blood glucose levels of the PWD falling below a threshold low bloodglucose level). In these and other cases, in addition to or in place ofadjusting the BBR, other user-specific dosage-guidelines can beadjusted. For example, ISF and CR can also be adjusted according to thepresent disclosure. In some cases, if BBR is adjusted higher, ISF may beadjusted higher by the same or an approximately proportional percentageamount and CR may be adjusted lower by the same or an approximatelyproportional percentage amount of the BBR.

At block X-750, insulin may be delivered during the related diurnal timeperiod based on the adjusted baseline basal insulin rate. For example,the insulin pump can deliver insulin based on the adjusted baselinebasal insulin rate. In some cases, such delivery can include a controldevice (e.g., the control circuitry 240 of FIG. 2B) sending a message tothe insulin pump to deliver insulin.

Modifications, additions, or omissions may be made to the method X-700without departing from the scope of the present disclosure. For example,the operations of the method X-700 may be implemented in differingorder. Additionally or alternatively, two or more operations may beperformed at the same time. Furthermore, the outlined operations andactions are provided as examples, and some of the operations and actionsmay be optional, combined into fewer operations and actions, or expandedinto additional operations and actions without detracting from theessence of the disclosed embodiments.

FIG. 23 illustrates a flowchart of an example method X-800 of utilizinga fear of hypoglycemia index. The method X-800 may be performed by anysuitable system, apparatus, or device. For example, the system 10, thepump assembly 15, the mobile computing device 60 of FIG. 1, and/or aremote server may perform one or more of the operations associated withthe method X-800. Although illustrated with discrete blocks, the stepsand operations associated with one or more of the blocks of the methodX-800 may be divided into additional blocks, combined into fewer blocks,or eliminated, depending on the desired implementation.

At block X-810, an interface can be displayed to a user to input an FHI.For example, an interface can be displayed on a mobile computing device(e.g., the mobile computing device 60 of FIG. 1) and/or to a terminalconnected over a network such as the internet to a remote server. Insome cases, the user (e.g., a PWD or a healthcare professional) can bepresented with an interactive feature from which the user can select theFHI. In these and other cases, the interface can include a variety ofways that the user can input the FHI, such as a preferred blood glucoselevel, a preferred probability of going above or below a certainthreshold, a textual description of a blood glucose level (e.g., “preferhigh”), etc. In these and other cases, the FHI can correspond to athreshold blood glucose level and an acceptable probability of crossingthe threshold blood glucose level. For example, “prefer high” maydesignate a low threshold blood glucose level as 100 mg/dl, with atarget blood glucose level of 150 mg/dl, and a high threshold bloodglucose level of 220 mg/dl, and an acceptable probability of 5% forexceeding either the low or the high threshold values.

At block X-820, a probability of a PWD crossing a threshold bloodglucose level is calculated. For example, a calculation can be made asto how likely the PWD is to cross the threshold blood glucose levelcorresponding to the FHI. In these and other cases, the probability ofcrossing the threshold can also be compared to the acceptableprobability of crossing the threshold. For example, if the FHI indicatesthat a 5% probability of exceeding a threshold is acceptable, thecalculated probability of exceeding the threshold can be compared to the5% acceptable probability.

At block X-830, target blood glucose level can be modified to moreclosely align the probability of crossing the threshold with the FHI.For example, if the probability of dropping below a threshold is higherthan the acceptable probability, the target blood glucose level may beadjusted higher such that the probability is closer to the acceptableprobability. In some cases, the target blood glucose level can beadjusted such that the probability of crossing the threshold is the sameas the acceptable probability. In these and other cases, themodification of the baseline basal insulin rate can also be based on theactual insulin delivered compared to the BBR for a diurnal period. Forexample, if four delivery actions occur during a diurnal time period andeach of them deliver 2× the BBR, the BBR can be modified based on boththe FHI and the 2× delivered. Continuing the example, if a user hadselected a low FHI (e.g., the PWD is not as concerned about going low),the target blood glucose level can be changed by a large amount (e.g.,between 0% and 5%) while if the user had selected a high FHI (e.g., thePWD is concerned about going low), the BBR can be changed be a smalleramount (e.g., between 0% and 2%). In these and other cases, the changeamount can vary depending on whether it is adjusting up or down. Forexample, for a PWD that prefers high blood glucose levels, methods andsystems of the present disclosure can use a larger change when adjustingthe BBR upwards and a lower change when adjusting the BBR downwards. Insome cases, increases to the target blood glucose level can beunconstrained, but decreases constrained to 5% or less, 3% or less, 2%or less, or 1% or less.

At block X-840, insulin can be delivered based on the modified targetblood glucose level. For example, a control device can determine insulindelivery profiles or rates based the target blood glucose level(s) usingany suitable method, including the methods described above. In somecases, the delivery of insulin can be based off of one or more insulindelivery profiles that can be generated, and selecting one of theprofiles that most closely approximates a target blood glucose level. Inthese and other cases, the actions of the delivery profiles can be aratio of the modified BBR. For example, the delivery actions can includeone of delivering 0×, 1×, or 2× the modified BBR.

In some cases, the delivery actions of the delivery profiles can bebased off of the FHI as well. For example, for a first FHI (e.g., thePWD is concerned about going low), the possible ratios used in thedelivery actions of the profile can include 0×, 0.5×, 1× and 1.5× theBBR (e.g., for a PWD that prefers low), while for a second FHI, thepossible ratios used in the delivery actions of the profile can include0×, 1×, 2×, and 3× (e.g., for a PWD that prefers high).

Modifications, additions, or omissions may be made to the method X-800without departing from the scope of the present disclosure. For example,the operations of the method X-800 may be implemented in differingorder. Additionally or alternatively, two or more operations may beperformed at the same time. Furthermore, the outlined operations andactions are provided as examples, and some of the operations and actionsmay be optional, combined into fewer operations and actions, or expandedinto additional operations and actions without detracting from theessence of the disclosed embodiments.

FIG. 24 illustrates a flowchart of an example method X-900 of utilizinga temporary override. The method X-900 may be performed by any suitablesystem, apparatus, or device. For example, the system 10, the pumpassembly 15, the mobile computing device 60 of FIG. 1, and/or a remoteserver may perform one or more of the operations associated with themethod X-900. Although illustrated with discrete blocks, the steps andoperations associated with one or more of the blocks of the method X-900may be divided into additional blocks, combined into fewer blocks, oreliminated, depending on the desired implementation.

At block X-910, a set of insulin delivery profiles may be generated,each having a series of insulin delivery actions. For example, anelectronic device (e.g., the pump assembly 15, the mobile computingdevice 60 of FIG. 1 and/or a remote server) may generate a set ofprofiles in accordance with the present disclosure.

At block X-920, an input indicating a temporary override may bereceived. The temporary override can indicate a user-preferred bloodglucose level for one or more diurnal periods. For example, a user(e.g., a PWD) may be presented with a field or other entry componentwhere the user can enter a numerical blood glucose level for a setperiod of time. As another example, the user may be presented withmultiple activities (e.g., exercising, driving a car for an extendedperiod of time, etc.) and when the activity will be performed. Asanother example, the user may be presented with a series of textualdescriptions of preferred blood glucose levels (e.g., “do not go low,”or “do not go high”). In these and other cases, the user may be limitedin selecting a temporary override for a period of time some point in thefuture (e.g., at least thirty minutes in the future).

At block X-930, a log of the temporary override can be generated. Forexample, the electronic device can record what was selected for thetemporary override (e.g., a target blood glucose level, a particularactivity, etc.), when, and/or for how long. In some cases, the log maybe updated each time the user inputs a temporary override.

At block X-940, a baseline basal insulin rate (BBR) can be temporarilymodified based on the temporary override. For example, the BBR can bemodified to more closely align the BBR with the user-preferred bloodglucose level. For example, the BBR can be adjusted higher if thetemporary override indicates a lower than normal blood glucose level. Asanother example, the BBR can be adjusted lower if the temporary overrideindicates a higher than normal blood glucose level. In some cases, thetemporary override from the block X-920 can be received and the BBR canbe modified prior to generating the set of profiles, or the set ofprofiles can be updated after the temporary override is received and/orthe BBR is modified.

At block X-950, a determination can be made as to which profile from theset of profiles approximates the user-preferred blood glucose levelduring the diurnal period. For example, a predicted blood glucose levelfor various points in time can be projected based on each of theprofiles. The variation from the user-preferred blood glucose level canbe analyzed, for example, by accumulating the variation over time andfinding the profile with the lowest variation from the user-preferredblood glucose level. In these and other cases, the profile that mostclosely approximates the user-preferred blood glucose level can beselected as the basis for delivery actions of insulin.

At block X-960, insulin can be delivered based on the next action in theselected profile. For example, a given profile that was selected mighthave sixteen delivery actions spanning four hours, and the first ofsixteen actions may be taken to deliver insulin. In some cases, theblock X-960 can include control circuitry or another control devicegenerating a message to be provided to a pump to deliver insulin inaccordance with the next action in the selected profile.

At block X-970, the log can be periodically provided to a healthcareprofessional. For example, the log generated and/or updated at blockX-930 can be sent to a healthcare professional using email, textmessage, via an app, etc., such that the healthcare professional canreview the overrides that have occurred for a PWD.

At block X-980, the log can be parsed to determine if a pattern ispresent in the temporary overrides. For example, the PWD may input atemporary override every Monday, Wednesday, and Friday from 6 PM to 7 PMwhen they exercise. As another example, the PWD may input a temporaryoverride Monday through Friday from 5:30 PM until 6:15 PM while the PWDdrives home from work. The log can be parsed to find such patterns ofoverrides.

At block X-990, the baseline basal insulin rate can be modified for agiven diurnal period based on the pattern. Following the first examplegiven at block X-980, methods and systems of the present disclosure canadjust the BBR for 6 PM to 7 PM on Monday, Wednesday and Friday based onthe repeated overrides occurring at those times. Following the secondexample given above at block X-980, methods and systems of the presentdisclosure can adjust the BBR from 5:30 PM to 6:15 PM Monday throughFriday based on the repeated overrides for that span of time.

Modifications, additions, or omissions may be made to the method X-900without departing from the scope of the present disclosure. For example,the operations of the method X-900 may be implemented in differing order(e.g., the block X-920 can be performed after the block X-910, and/orthe blocks X-970 and/or X-980 can be performed any time after the blockX-930). Additionally or alternatively, two or more operations may beperformed at the same time. Furthermore, the outlined operations andactions are provided as examples, and some of the operations and actionsmay be optional (e.g., the blocks X-930, X-940, X-970, X-980, and/orX-990), combined into fewer operations and actions, or expanded intoadditional operations and actions without detracting from the essence ofthe disclosed embodiments.

The embodiments described herein may include the use of aspecial-purpose or general-purpose computer including various computerhardware or software modules, as discussed in greater detail below.

Embodiments described herein may be implemented using computer-readablemedia for carrying or having computer-executable instructions or datastructures stored thereon. Such computer-readable media may be anyavailable media that may be accessed by a general-purpose orspecial-purpose computer. By way of example, and not limitation, suchcomputer-readable media may include non-transitory computer-readablestorage media including Random-Access Memory (RAM), Read-Only Memory(ROM), Electrically Erasable Programmable Read-Only Memory (EEPROM),Compact Disc Read-Only Memory (CD-ROM) or other optical disk storage,magnetic disk storage or other magnetic storage devices, flash memorydevices (e.g., solid state memory devices), or any other storage mediumwhich may be used to carry or store desired program code in the form ofcomputer-executable instructions or data structures and which may beaccessed by a general-purpose or special-purpose computer. Combinationsof the above may also be included within the scope of computer-readablemedia.

Computer-executable instructions comprise, for example, instructions anddata which cause a general-purpose computer, special-purpose computer,or special-purpose processing device (e.g., one or more processors) toperform a certain function or group of functions. Although the subjectmatter has been described in language specific to structural featuresand/or methodological acts, it is to be understood that the subjectmatter defined in the appended claims is not necessarily limited to thespecific features or acts described above. Rather, the specific featuresand acts described above are disclosed as example forms of implementingthe claims.

As used herein, the terms “module” or “component” may refer to specifichardware implementations configured to perform the operations of themodule or component and/or software objects or software routines thatmay be stored on and/or executed by general-purpose hardware (e.g.,computer-readable media, processing devices, etc.) of the computingsystem. In some embodiments, the different components, modules, engines,and services described herein may be implemented as objects or processesthat execute on the computing system (e.g., as separate threads). Whilesome of the systems and methods described herein are generally describedas being implemented in software (stored on and/or executed bygeneral-purpose hardware), specific hardware implementations or acombination of software and specific hardware implementations are alsopossible and contemplated. In the present description, a “computingentity” may be any computing system as previously defined herein, or anymodule or combination of modulates running on a computing system.

Any ranges expressed herein (including in the claims) are considered tobe given their broadest possible interpretation. For example, unlessexplicitly mentioned otherwise, ranges are to include their end points(e.g., a range of “between X and Y” would include X and Y).Additionally, ranges described using the terms “approximately” or“about” are to be understood to be given their broadest meaningconsistent with the understanding of those skilled in the art.Additionally, the term approximately includes anything within 10%, or5%, or within manufacturing or typical tolerances.

All examples and conditional language recited herein are intended forpedagogical objects to aid the reader in understanding the disclosureand the concepts contributed by the inventor to furthering the art, andare to be construed as being without limitation to such specificallyrecited examples and conditions. Although embodiments of the presentdisclosure have been described in detail, it should be understood thatthe various changes, substitutions, and alterations could be made heretowithout departing from the spirit and scope of the disclosure.

What is claimed is:
 1. A system, comprising: an insulin delivery deviceconfigured to deliver insulin to a user of the insulin delivery device;and an insulin delivery control unit associated with the insulindelivery device, wherein the insulin delivery control unit is configuredto: determining a shelf-life risk score for undelivered insulin withinthe insulin delivery device; and based on the shelf-life risk scoreexceeding a threshold, enabling a lock-out mode for locking outautomated modification of a baseline basal insulin rate for the user ofthe insulin delivery device until the insulin delivery device has freshinsulin.
 2. The system of claim 1, wherein the shelf-life risk score isat least based on an age of the undelivered insulin.
 3. The system ofclaim 1, wherein determining a shelf-life risk score for undeliveredinsulin within the insulin delivery device comprises receiving ashelf-life risk score from a diabetes management system.
 4. The systemof claim 1, wherein the insulin delivery control unit controls theinsulin delivery device to: deliver insulin at a first point in timeaccording to a first insulin delivery action of a first series ofinsulin delivery actions of a first insulin delivery profile, the firstseries of insulin delivery actions including at least one action thatincludes delivering insulin at a rate larger than the baseline basalinsulin rate; and deliver insulin at a second point in time according toa first insulin delivery action of a second series of insulin deliveryactions of a second insulin delivery profile, the second series ofinsulin delivery actions including delivering insulin at the rate largerthan the baseline basal insulin rate, wherein the shelf-life risk scoreis further based on a number of times insulin is delivered at the ratelarger than the baseline basal insulin rate exceeding a threshold. 5.The system of claim 1, further comprising a diabetes management systemassociated with the insulin delivery device and configured to trackundelivered insulin within the insulin delivery device.
 6. The system ofclaim 5, wherein tracking undelivered insulin within the insulindelivery device comprises tracking an age of undelivered insulin.
 7. Thesystem of claim 5, wherein the diabetes management system associatedwith the insulin delivery device is configured to track insulin deliveryactions of the insulin delivery device.
 8. The system of claim 7,wherein the diabetes management system associated with the insulindelivery device is configured to determine a frequency of an insulindelivery action of the tracked insulin delivery actions, wherein theinsulin delivery action comprises delivering a dose of insulin thatexceeds a threshold.
 9. The system of claim 8, wherein the threshold isa multiple or a fraction of the baseline basal insulin rate.
 10. Amethod comprising: determining a shelf-life risk score for undeliveredinsulin within an insulin delivery device; and based on the shelf-liferisk score exceeding a threshold, locking out automated modification ofa baseline basal insulin rate for a user of the insulin delivery deviceuntil the insulin delivery device has fresh insulin.
 11. The method ofclaim 10, wherein the shelf-life risk score is at least based on an ageof the undelivered insulin.
 12. The method of claim 10, whereindetermining the shelf-life risk score for undelivered insulin within theinsulin delivery device comprises receiving a shelf-life risk score froma diabetes management system.
 13. The method of claim 10, furthercomprising: delivering insulin at a first point in time according to afirst insulin delivery action of a first series of insulin deliveryactions of a first insulin delivery profile, the first series of insulindelivery actions including at least one action that includes deliveringinsulin at a rate larger than the baseline basal insulin rate; anddelivering insulin at a second point in time according to a firstinsulin delivery action of a second series of insulin delivery actionsof a second insulin delivery profile, the second series of insulindelivery actions including delivering insulin at the rate larger thanthe baseline basal insulin rate; wherein the shelf-life risk score isfurther based on a number of times insulin is delivered at the ratelarger than the baseline basal insulin rate exceeding a threshold. 14.The method of claim 10, wherein locking out automated modification of abaseline basal insulin rate prevents modification of the baseline basalinsulin rate upwards while allowing automated modification of thebaseline basal insulin rate downwards.
 15. The method of claim 10,further comprising generating a message that the shelf-life risk scorehas exceeded the threshold.